W0710 21:11:36.361000 836589 /mnt/lustre/slurm/users/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/distributed/run.py:766] W0710 21:11:36.361000 836589 /mnt/lustre/slurm/users/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/distributed/run.py:766] ***************************************** W0710 21:11:36.361000 836589 /mnt/lustre/slurm/users/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0710 21:11:36.361000 836589 /mnt/lustre/slurm/users/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/distributed/run.py:766] ***************************************** 2025-07-10 21:13:09.418238: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418244: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418241: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418250: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418257: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418252: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418256: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:09.418249: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-07-10 21:13:10.932610: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932608: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932603: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932600: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932607: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932610: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932612: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-07-10 21:13:10.932615: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1752149591.527157 836605 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527155 836600 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527165 836603 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527164 836604 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527160 836602 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527171 836599 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527169 836601 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.527157 836598 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1752149591.808884 836601 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808888 836604 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808890 836599 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808893 836600 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808895 836602 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808895 836605 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808904 836598 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered E0000 00:00:1752149591.808907 836603 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1752149593.276631 836604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276642 836600 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276635 836601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276641 836602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276644 836598 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276629 836599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276647 836605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276646 836603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276691 836604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276708 836600 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276718 836601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276734 836602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276742 836598 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276750 836599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276765 836605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276771 836603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276776 836604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276782 836600 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276787 836601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276793 836602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276798 836598 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276803 836599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276809 836605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276814 836603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276820 836604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276825 836600 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276830 836601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276836 836602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276841 836598 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276847 836599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276862 836605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1752149593.276867 836603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. 2025-07-10 21:13:13.446096: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446097: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446106: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446101: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446106: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446101: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446106: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-07-10 21:13:13.446110: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. [2025-07-10 21:14:29,621] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:14:29,622] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,855] [INFO] [comm.py:652:init_distributed] cdb=None [2025-07-10 21:15:07,858] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl Map (num_proc=8): 0%| | 0/302 [00:00 using: using: using: using: using: using: using: [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 [2025-07-10 21:15:10,057] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 8 You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. [2025-07-10 21:15:12,572] [INFO] [partition_parameters.py:348:__exit__] finished initializing model - num_params = 825, num_elems = 4.07B Loading checkpoint shards: 0%| | 0/2 [00:00 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] communication_data_type ...... None [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}} [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] curriculum_enabled_legacy .... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] curriculum_params_legacy ..... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}} [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] data_efficiency_enabled ...... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] dataloader_drop_last ......... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] disable_allgather ............ False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] dump_state ................... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] dynamic_loss_scale_args ...... None [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_enabled ........... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_gas_boundary_resolution 1 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_layer_name ........ bert.encoder.layer [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_layer_num ......... 0 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_max_iter .......... 100 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_stability ......... 1e-06 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_tol ............... 0.01 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] eigenvalue_verbose ........... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] elasticity_enabled ........... False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] flops_profiler_config ........ { "enabled": false, "recompute_fwd_factor": 0.0, "profile_step": 1, "module_depth": -1, "top_modules": 1, "detailed": true, "output_file": null } [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] fp16_auto_cast ............... None [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] fp16_enabled ................. False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] fp16_master_weights_and_gradients False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] global_rank .................. 0 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] grad_accum_dtype ............. None [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] gradient_accumulation_steps .. 2 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] gradient_clipping ............ 1.0 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] gradient_predivide_factor .... 1.0 [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] graph_harvesting ............. False [2025-07-10 21:16:20,087] [INFO] [config.py:1003:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] initial_dynamic_scale ........ 1 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] load_universal_checkpoint .... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] loss_scale ................... 1.0 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] memory_breakdown ............. False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] mics_hierarchial_params_gather False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] mics_shard_size .............. -1 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] nebula_config ................ { "enabled": false, "persistent_storage_path": null, "persistent_time_interval": 100, "num_of_version_in_retention": 2, "enable_nebula_load": true, "load_path": null } [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] optimizer_legacy_fusion ...... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] optimizer_name ............... None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] optimizer_params ............. None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True} [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] pld_enabled .................. False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] pld_params ................... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] prescale_gradients ........... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] scheduler_name ............... None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] scheduler_params ............. None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] seq_parallel_communication_data_type torch.float32 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] sparse_attention ............. None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] sparse_gradients_enabled ..... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] steps_per_print .............. inf [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] timers_config ................ enabled=True synchronized=True [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] train_batch_size ............. 16 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] train_micro_batch_size_per_gpu 1 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] use_data_before_expert_parallel_ False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] use_node_local_storage ....... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] wall_clock_breakdown ......... False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] weight_quantization_config ... None [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] world_size ................... 8 [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] zero_allow_untested_optimizer False [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100000000, max_in_cpu=1000000000, pin_memory=True) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=True, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50000000 param_persistence_threshold=100000 model_persistence_threshold=9223372036854775807 max_live_parameters=1000000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=True use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] zero_enabled ................. True [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] zero_force_ds_cpu_optimizer .. True [2025-07-10 21:16:20,088] [INFO] [config.py:1003:print] zero_optimization_stage ...... 3 [2025-07-10 21:16:20,088] [INFO] [config.py:989:print_user_config] json = { "fp16": { "enabled": false, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "bf16": { "enabled": true }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "none", "pin_memory": true }, "offload_param": { "device": "none", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1.000000e+09, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1.000000e+09, "stage3_max_reuse_distance": 1.000000e+09, "stage3_gather_16bit_weights_on_model_save": true }, "gradient_accumulation_steps": 2, "gradient_clipping": 1.0, "steps_per_print": inf, "train_batch_size": 16, "train_micro_batch_size_per_gpu": 1, "wall_clock_breakdown": false, "zero_optimization.reduce_bucket_size": 4.194304e+06, "zero_optimization.stage3_param_persistence_threshold": 2.048000e+04, "zero_optimization.stage3_prefetch_bucket_size": 3.774874e+06 } Gradient accumulation steps mismatch: GradientAccumulationPlugin has 1, DeepSpeed config has 2. Using DeepSpeed's value. Parameter Offload: Total persistent parameters: 755712 in 408 params /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. warnings.warn( # warn only once 0%| | 0/1208 [00:00 and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt box diff coord reward error closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.25 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2619: cache has only 0 modules [Step 0] loss_orig = 0.539945, loss_refine = -1.984051[Step 0] loss_orig = 0.539945, loss_refine = 0.661350[Step 0] loss_orig = -1.619835, loss_refine = -0.661350 [Step 0] loss_orig = -1.619835, loss_refine = 0.661350 [Step 0] loss_orig = 0.539945, loss_refine = 0.661350 [Step 0] loss_orig = 0.539945, loss_refine = -0.661350 [Step 0] loss_orig = 0.539945, loss_refine = 0.661350 [Step 0] loss_orig = 0.539945, loss_refine = 0.661350 Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 3492: cache has only 0 modules 0%| | 1/1208 [01:50<36:55:39, 110.14s/it] {'loss': -0.0, 'grad_norm': 7.080481916390073, 'learning_rate': 9.991721854304635e-07, 'completion_length': 127.25, 'rewards/accuracy_reward_action': 0.4166666666666667, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 1.5416666666666667, 'reward_std': 0.6844539841016134, 'kl': 0.0, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.01} 0%| | 1/1208 [01:50<36:55:39, 110.14s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 4365: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 5238: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Height'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxdiff coord reward error diff coord reward error Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 6111: cache has only 0 modules [Step 1] loss_orig = -0.539920, loss_refine = 0.479848[Step 1] loss_orig = -0.539920, loss_refine = -1.055578 [Step 1] loss_orig = -0.539913, loss_refine = 0.479862 [Step 1] loss_orig = 1.619896, loss_refine = -0.287761 [Step 1] loss_orig = -0.539852, loss_refine = -1.055576 [Step 1] loss_orig = -0.539900, loss_refine = -1.055566 [Step 1] loss_orig = -0.539929, loss_refine = 1.247554 [Step 1] loss_orig = 1.619859, loss_refine = 1.247548 0%| | 2/1208 [03:05<30:02:54, 89.70s/it] {'loss': 0.0, 'grad_norm': 7.228841296866502, 'learning_rate': 9.98344370860927e-07, 'completion_length': 121.625, 'rewards/accuracy_reward_action': 0.625, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 0.8333333333333334, 'reward': 1.625, 'reward_std': 0.8970667918523153, 'kl': 0.0009555816650390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.01} 0%| | 2/1208 [03:05<30:02:54, 89.70s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 6984: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt boxcloser to gt box closer to gt box closer to gt box diff coord reward error diff coord reward error diff coord reward error Reward function name: accuracy_reward_action Reward: 0.125 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.5 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 7857: cache has only 0 modules [Step 2] loss_orig = -0.724422, loss_refine = -0.194974[Step 2] loss_orig = -0.724360, loss_refine = -1.755129[Step 2] loss_orig = -0.724390, loss_refine = -0.194999[Step 2] loss_orig = -0.724395, loss_refine = -0.195008 [Step 2] loss_orig = -0.724423, loss_refine = -0.194982 [Step 2] loss_orig = 1.207410, loss_refine = -0.194981 [Step 2] loss_orig = 1.207414, loss_refine = 1.365139 [Step 2] loss_orig = 1.207421, loss_refine = 1.365142 Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 8730: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - System'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box diff coord reward error diff coord reward error Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 9603: cache has only 0 modules [Step 2] loss_orig = -0.724377, loss_refine = -0.479789 [Step 2] loss_orig = -0.724406, loss_refine = -1.247518[Step 2] loss_orig = -0.724412, loss_refine = 1.055641 [Step 2] loss_orig = 1.207396, loss_refine = -0.479776 [Step 2] loss_orig = 1.207423, loss_refine = -1.247507 [Step 2] loss_orig = -0.724396, loss_refine = 0.287903 [Step 2] loss_orig = 1.207418, loss_refine = 1.055610 [Step 2] loss_orig = -0.724397, loss_refine = 1.055634 0%| | 3/1208 [04:54<32:58:55, 98.54s/it] {'loss': 0.0, 'grad_norm': 5.305439703631956, 'learning_rate': 9.975165562913907e-07, 'completion_length': 144.84375, 'rewards/accuracy_reward_action': 0.3125, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.6875, 'reward': 1.125, 'reward_std': 0.7446096241474152, 'kl': 0.0007839202880859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.02} 0%| | 3/1208 [04:54<32:58:55, 98.54s/it]Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 0.125 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 10476: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element deserts'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward errordiff coord reward error closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.25 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 11349: cache has only 0 modules [Step 3] loss_orig = 0.000078, loss_refine = 0.526236 [Step 3] loss_orig = 0.000051, loss_refine = 1.368110[Step 3] loss_orig = 0.000095, loss_refine = -0.315675 [Step 3] loss_orig = 0.000027, loss_refine = -1.157493 [Step 3] loss_orig = 1.870500, loss_refine = -0.315625 [Step 3] loss_orig = 0.000089, loss_refine = -1.157428 [Step 3] loss_orig = -1.870437, loss_refine = 1.368054 [Step 3] loss_orig = 0.000060, loss_refine = -0.315651 Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 12222: cache has only 0 modules 0%| | 4/1208 [06:14<30:31:30, 91.27s/it] {'loss': 0.0001, 'grad_norm': 14.481886285158794, 'learning_rate': 9.966887417218542e-07, 'completion_length': 121.54166666666667, 'rewards/accuracy_reward_action': 0.2916666666666667, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 0.875, 'reward': 1.5416666666666667, 'reward_std': 0.9304341276486715, 'kl': 0.001255035400390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.03} 0%| | 4/1208 [06:14<30:31:30, 91.27s/it]Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 13095: cache has only 0 modules Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 13968: cache has only 0 modules 0%| | 5/1208 [06:56<24:29:45, 73.30s/it] {'loss': 0.0, 'grad_norm': 11.833166630392851, 'learning_rate': 9.958609271523178e-07, 'completion_length': 111.5625, 'rewards/accuracy_reward_action': 0.6875, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 1.875, 'reward_std': 0.7376964688301086, 'kl': 0.001148223876953125, 'clip_ratio': 0.0, 'epoch': 0.03} 0%| | 5/1208 [06:56<24:29:45, 73.30s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 14841: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more information'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 15714: cache has only 0 modules [Step 5] loss_orig = 1.619995, loss_refine = -0.882745[Step 5] loss_orig = -0.539763, loss_refine = 1.135114[Step 5] loss_orig = -0.539786, loss_refine = -0.882735[Step 5] loss_orig = -0.539586, loss_refine = -0.882796 [Step 5] loss_orig = -0.539801, loss_refine = 1.135179 [Step 5] loss_orig = -0.539869, loss_refine = 1.135146 [Step 5] loss_orig = 1.619916, loss_refine = -0.882778 [Step 5] loss_orig = -0.539870, loss_refine = 0.126161 Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 16587: cache has only 0 modules 0%| | 6/1208 [07:47<22:01:43, 65.98s/it] {'loss': 0.0001, 'grad_norm': 32.236419734500224, 'learning_rate': 9.950331125827814e-07, 'completion_length': 94.0, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.6982704202334086, 'kl': 0.002750396728515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.04} 0%| | 6/1208 [07:47<22:01:43, 65.98s/it]Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 17460: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sign in - Google Accounts'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 18333: cache has only 0 modules [Step 6] loss_orig = -0.724135, loss_refine = -0.829374[Step 6] loss_orig = -0.724367, loss_refine = 0.645173[Step 6] loss_orig = 1.207448, loss_refine = -1.566724 [Step 6] loss_orig = -0.724295, loss_refine = -0.829442[Step 6] loss_orig = -0.724374, loss_refine = 0.645335 [Step 6] loss_orig = -0.724285, loss_refine = -0.092135 [Step 6] loss_orig = 1.207420, loss_refine = 0.645201 [Step 6] loss_orig = 1.207486, loss_refine = 1.382476 Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 19206: cache has only 0 modules 1%| | 7/1208 [08:56<22:18:48, 66.88s/it] {'loss': 0.0001, 'grad_norm': 24.400726766334536, 'learning_rate': 9.94205298013245e-07, 'completion_length': 106.16666666666667, 'rewards/accuracy_reward_action': 0.4583333333333333, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 1.6666666666666667, 'reward_std': 0.8765602707862854, 'kl': 0.00286102294921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.05} 1%| | 7/1208 [08:56<22:18:48, 66.88s/it]Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 20079: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 20952: cache has only 0 modules 1%| | 8/1208 [09:43<20:07:33, 60.38s/it] {'loss': 0.0001, 'grad_norm': 11.674897377700885, 'learning_rate': 9.933774834437085e-07, 'completion_length': 111.0625, 'rewards/accuracy_reward_action': 0.75, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.9375, 'reward': 2.0625, 'reward_std': 0.8152145147323608, 'kl': 0.00258636474609375, 'clip_ratio': 0.0, 'epoch': 0.05} 1%| | 8/1208 [09:43<20:07:33, 60.38s/it]Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 21825: cache has only 0 modules Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 22698: cache has only 0 modules ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Fit to page'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. ⚠️ Annotation failed, using original image. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt boxcloser to gt box closer to gt box closer to gt box diff coord reward error closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.25 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 23571: cache has only 0 modules [Step 8] loss_orig = -1.206990, loss_refine = -1.134968 [Step 8] loss_orig = 0.724548, loss_refine = -1.134851 [Step 8] loss_orig = -1.207318, loss_refine = -0.126041 [Step 8] loss_orig = -1.207037, loss_refine = -0.125941 [Step 8] loss_orig = 0.724577, loss_refine = 0.882954 [Step 8] loss_orig = 0.724838, loss_refine = -0.126005 [Step 8] loss_orig = 0.724488, loss_refine = -0.126088 [Step 8] loss_orig = 0.724522, loss_refine = 1.891819 1%| | 9/1208 [11:01<21:59:43, 66.04s/it] {'loss': 0.0001, 'grad_norm': 6.594472751238751, 'learning_rate': 9.92549668874172e-07, 'completion_length': 126.75, 'rewards/accuracy_reward_action': 0.5, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 0.9166666666666666, 'reward': 1.9166666666666667, 'reward_std': 0.7548364400863647, 'kl': 0.0041656494140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 0.06} 1%| | 9/1208 [11:01<21:59:43, 66.04s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 24444: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 25317: cache has only 0 modules [Step 9] loss_orig = -0.503808, loss_refine = -0.539839[Step 9] loss_orig = 0.840002, loss_refine = 1.619954 [Step 9] loss_orig = -0.503703, loss_refine = -0.539847 [Step 9] loss_orig = 2.183844, loss_refine = -0.539799 [Step 9] loss_orig = -0.503681, loss_refine = -0.539887 [Step 9] loss_orig = -0.503734, loss_refine = 1.619869[Step 9] loss_orig = -0.503739, loss_refine = -0.539866 [Step 9] loss_orig = -0.503797, loss_refine = -0.539846 Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 26190: cache has only 0 modules 1%| | 10/1208 [12:20<23:15:42, 69.90s/it] {'loss': 0.0001, 'grad_norm': 15.689400399376387, 'learning_rate': 9.917218543046357e-07, 'completion_length': 135.83333333333334, 'rewards/accuracy_reward_action': 0.8333333333333334, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.875, 'reward': 2.0, 'reward_std': 0.6503192186355591, 'kl': 0.00370025634765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 0.07} 1%| | 10/1208 [12:20<23:15:42, 69.90s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 27063: cache has only 0 modules Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 27936: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Track'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 28809: cache has only 0 modules [Step 10] loss_orig = 0.935387, loss_refine = -0.266166[Step 10] loss_orig = -0.934960, loss_refine = 0.443988[Step 10] loss_orig = -0.935121, loss_refine = -0.976518 [Step 10] loss_orig = 0.935383, loss_refine = 0.443992[Step 10] loss_orig = 0.935339, loss_refine = 0.443952[Step 10] loss_orig = 0.935320, loss_refine = -0.976235 [Step 10] loss_orig = -0.934894, loss_refine = -0.976402 [Step 10] loss_orig = -0.934798, loss_refine = 1.864499 1%| | 11/1208 [13:38<24:07:08, 72.54s/it] {'loss': 0.0002, 'grad_norm': 11.969062674153326, 'learning_rate': 9.908940397350992e-07, 'completion_length': 110.20833333333333, 'rewards/accuracy_reward_action': 0.7916666666666666, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.0833333333333335, 'reward_std': 0.7653205891450247, 'kl': 0.0054779052734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.07} 1%| | 11/1208 [13:38<24:07:08, 72.54s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 29682: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box diff coord reward error closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 30555: cache has only 0 modules [Step 11] loss_orig = -0.661223, loss_refine = -0.467567[Step 11] loss_orig = 1.984144, loss_refine = -0.467377 [Step 11] loss_orig = 0.661866, loss_refine = -0.467506 [Step 11] loss_orig = -0.661235, loss_refine = 0.467824 [Step 11] loss_orig = -0.661268, loss_refine = 1.403033 [Step 11] loss_orig = 0.661556, loss_refine = -0.467566 [Step 11] loss_orig = -0.661162, loss_refine = 1.403092 [Step 11] loss_orig = -0.661241, loss_refine = -1.402910 Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 31428: cache has only 0 modules 1%| | 12/1208 [15:03<25:23:24, 76.43s/it] {'loss': 0.0002, 'grad_norm': 12.49185737494912, 'learning_rate': 9.900662251655628e-07, 'completion_length': 129.91666666666666, 'rewards/accuracy_reward_action': 0.6666666666666666, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.8333333333333334, 'reward': 1.9583333333333333, 'reward_std': 0.7261757552623749, 'kl': 0.0060882568359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.08} 1%| | 12/1208 [15:03<25:23:24, 76.43s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 32301: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element LibreOffice Writer'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 33174: cache has only 0 modules [Step 12] loss_orig = 0.840134, loss_refine = 0.000227[Step 12] loss_orig = -0.503701, loss_refine = 1.322860[Step 12] loss_orig = -0.503844, loss_refine = 0.000269 [Step 12] loss_orig = -0.503183, loss_refine = 0.000180[Step 12] loss_orig = -0.503685, loss_refine = -1.322580 [Step 12] loss_orig = -0.503802, loss_refine = 0.000287 [Step 12] loss_orig = -0.503581, loss_refine = -1.322549 [Step 12] loss_orig = 2.183839, loss_refine = 1.322907 Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 34047: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Close pane'. Your previous answer was [{'action': 'click', 'coordinate': [2510, 87]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 34920: cache has only 0 modules [Step 12] loss_orig = -0.539869, loss_refine = 0.000299 [Step 12] loss_orig = -0.539642, loss_refine = 0.000129[Step 12] loss_orig = 1.620006, loss_refine = 0.000102 [Step 12] loss_orig = -0.539828, loss_refine = 0.000031 [Step 12] loss_orig = -0.539677, loss_refine = 0.000050[Step 12] loss_orig = -0.539771, loss_refine = 1.870637 [Step 12] loss_orig = -0.539863, loss_refine = 0.000045 [Step 12] loss_orig = 1.620368, loss_refine = -1.870369 1%| | 13/1208 [16:50<28:21:50, 85.45s/it] {'loss': 0.0002, 'grad_norm': 11.858605060955565, 'learning_rate': 9.892384105960264e-07, 'completion_length': 119.5625, 'rewards/accuracy_reward_action': 0.78125, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.9375, 'reward': 1.84375, 'reward_std': 0.6243463158607483, 'kl': 0.0060882568359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.09} 1%| | 13/1208 [16:50<28:21:50, 85.45s/it]Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 35793: cache has only 0 modules Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 36666: cache has only 0 modules 1%| | 14/1208 [17:28<23:36:04, 71.16s/it] {'loss': 0.0004, 'grad_norm': 6.819593302147197, 'learning_rate': 9.8841059602649e-07, 'completion_length': 87.375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5850084125995636, 'kl': 0.00970458984375, 'clip_ratio': 0.0, 'epoch': 0.09} 1%| | 14/1208 [17:28<23:36:04, 71.16s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 37539: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1350, 420]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 38412: cache has only 0 modules [Step 14] loss_orig = -0.353251, loss_refine = 0.000106[Step 14] loss_orig = -0.352688, loss_refine = -1.870222[Step 14] loss_orig = -0.352779, loss_refine = 0.000231 [Step 14] loss_orig = -0.353269, loss_refine = 0.000599 [Step 14] loss_orig = -0.352498, loss_refine = 0.000147 [Step 14] loss_orig = -0.353211, loss_refine = 0.000341 [Step 14] loss_orig = 2.474487, loss_refine = 0.000266 [Step 14] loss_orig = -0.353099, loss_refine = 1.870748 Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 39285: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: topic-link-a151002'. Your previous answer was [{'action': 'click', 'coordinate': [1564, 229]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 40158: cache has only 0 modules [Step 14] loss_orig = -0.353306, loss_refine = 0.353629[Step 14] loss_orig = -0.353145, loss_refine = -1.060455 [Step 14] loss_orig = 2.474627, loss_refine = 0.353563 [Step 14] loss_orig = -0.353358, loss_refine = -1.060339[Step 14] loss_orig = -0.353366, loss_refine = 0.353623 [Step 14] loss_orig = -0.353271, loss_refine = -1.060237 [Step 14] loss_orig = -0.353413, loss_refine = 0.353781 [Step 14] loss_orig = -0.353418, loss_refine = 1.767587 1%| | 15/1208 [19:16<27:14:11, 82.19s/it] {'loss': 0.0002, 'grad_norm': 6.964867764052209, 'learning_rate': 9.875827814569537e-07, 'completion_length': 131.875, 'rewards/accuracy_reward_action': 0.75, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.875, 'reward': 1.71875, 'reward_std': 0.5755723491311073, 'kl': 0.00787353515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.1875, 'epoch': 0.1} 1%| | 15/1208 [19:16<27:14:11, 82.19s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 41031: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'go to user account page'. Your previous answer was [{'action': 'click', 'coordinate': [2178, 301]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 41904: cache has only 0 modules [Step 15] loss_orig = 0.001390, loss_refine = -0.503787[Step 15] loss_orig = 0.000181, loss_refine = -0.503853 [Step 15] loss_orig = 0.000117, loss_refine = -0.503583 [Step 15] loss_orig = 0.000411, loss_refine = -0.503851[Step 15] loss_orig = 0.000257, loss_refine = -0.503441 [Step 15] loss_orig = 0.000176, loss_refine = -0.503842 [Step 15] loss_orig = 0.000114, loss_refine = 0.840536 [Step 15] loss_orig = 0.000069, loss_refine = 2.183877 Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 42777: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element New Photo Album...'. Your previous answer was [{'action': 'click', 'coordinate': [167, 106]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxdiff coord reward error closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 43650: cache has only 0 modules [Step 15] loss_orig = 2.474718, loss_refine = -1.501899 [Step 15] loss_orig = -0.353306, loss_refine = -0.643512 [Step 15] loss_orig = -0.353033, loss_refine = 0.215213 [Step 15] loss_orig = -0.353087, loss_refine = 0.214659 [Step 15] loss_orig = -0.353334, loss_refine = -0.643670 [Step 15] loss_orig = -0.353245, loss_refine = 0.214671 [Step 15] loss_orig = -0.353178, loss_refine = 0.214733 [Step 15] loss_orig = -0.353348, loss_refine = 1.931480 1%|▏ | 16/1208 [21:03<29:41:14, 89.66s/it] {'loss': 0.0002, 'grad_norm': 5.054452123297279, 'learning_rate': 9.867549668874173e-07, 'completion_length': 107.0625, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.90625, 'reward': 1.90625, 'reward_std': 0.6540238112211227, 'kl': 0.007659912109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.3125, 'epoch': 0.11} 1%|▏ | 16/1208 [21:03<29:41:14, 89.66s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 44523: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 45396: cache has only 0 modules [Step 16] loss_orig = -0.353112, loss_refine = -0.353229[Step 16] loss_orig = -0.353142, loss_refine = -0.353139 [Step 16] loss_orig = -0.352909, loss_refine = -0.353153[Step 16] loss_orig = -0.353116, loss_refine = -0.352594 [Step 16] loss_orig = -0.353201, loss_refine = -0.352943 [Step 16] loss_orig = -0.353113, loss_refine = -0.353032[Step 16] loss_orig = -0.353216, loss_refine = 2.474762 [Step 16] loss_orig = 2.474709, loss_refine = -0.353310 Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 46269: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 47142: cache has only 0 modules [Step 16] loss_orig = 0.000195, loss_refine = 0.150029[Step 16] loss_orig = 0.000220, loss_refine = 0.150128[Step 16] loss_orig = 0.000408, loss_refine = 0.149937[Step 16] loss_orig = 0.000348, loss_refine = 0.150005 [Step 16] loss_orig = 0.000267, loss_refine = -2.246035 [Step 16] loss_orig = 0.000865, loss_refine = 0.149963 [Step 16] loss_orig = 0.000287, loss_refine = 1.348120 [Step 16] loss_orig = 0.000382, loss_refine = 0.150114 1%|▏ | 17/1208 [22:25<28:53:38, 87.34s/it] {'loss': 0.0004, 'grad_norm': 9.376722247246404, 'learning_rate': 9.859271523178806e-07, 'completion_length': 104.0625, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 1.0, 'reward': 2.21875, 'reward_std': 0.3854074329137802, 'kl': 0.009185791015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 0.11} 1%|▏ | 17/1208 [22:25<28:53:38, 87.34s/it]Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 0.25 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 48015: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Thunderbird Mail'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt boxcloser to gt box closer to gt boxcloser to gt box diff coord reward error closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 48888: cache has only 0 modules [Step 17] loss_orig = 0.195271, loss_refine = -0.194840 [Step 17] loss_orig = 0.195204, loss_refine = -1.755068 [Step 17] loss_orig = 1.755224, loss_refine = -0.974772 [Step 17] loss_orig = -1.364231, loss_refine = 0.585339[Step 17] loss_orig = 0.195782, loss_refine = 0.585534 [Step 17] loss_orig = 0.195212, loss_refine = -0.194889 [Step 17] loss_orig = -1.364867, loss_refine = 0.585519 [Step 17] loss_orig = 0.195291, loss_refine = 1.365377 Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 49761: cache has only 0 modules ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image.⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image.⚠️ Annotation failed, using original image.⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box diff coord reward error diff coord reward error Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 50634: cache has only 0 modules [Step 17] loss_orig = -0.934804, loss_refine = -1.072608[Step 17] loss_orig = 0.935769, loss_refine = -0.213514 [Step 17] loss_orig = -0.934975, loss_refine = -1.072232 [Step 17] loss_orig = 0.935563, loss_refine = -0.214017 [Step 17] loss_orig = -0.934693, loss_refine = -0.214247 [Step 17] loss_orig = -0.934684, loss_refine = 1.502134 [Step 17] loss_orig = 0.935555, loss_refine = 1.502431 [Step 17] loss_orig = 0.935761, loss_refine = -0.214242 1%|▏ | 18/1208 [24:13<31:00:37, 93.81s/it] {'loss': 0.0004, 'grad_norm': 9.004271635107559, 'learning_rate': 9.850993377483442e-07, 'completion_length': 120.65625, 'rewards/accuracy_reward_action': 0.40625, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.84375, 'reward': 1.53125, 'reward_std': 0.905524268746376, 'kl': 0.00994873046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.12} 1%|▏ | 18/1208 [24:13<31:00:37, 93.81s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 51507: cache has only 0 modules Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 52380: cache has only 0 modules 2%|▏ | 19/1208 [24:44<24:41:20, 74.75s/it] {'loss': 0.0003, 'grad_norm': 7.951773392290347, 'learning_rate': 9.84271523178808e-07, 'completion_length': 94.3125, 'rewards/accuracy_reward_action': 0.75, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.7315178513526917, 'kl': 0.0084228515625, 'clip_ratio': 0.0, 'epoch': 0.13} 2%|▏ | 19/1208 [24:44<24:41:20, 74.75s/it]Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 53253: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 54126: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: RightScrollButton'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 54999: cache has only 0 modules [Step 19] loss_orig = -0.935041, loss_refine = 0.585231[Step 19] loss_orig = -0.934960, loss_refine = -0.974174[Step 19] loss_orig = 0.935485, loss_refine = -0.974464 [Step 19] loss_orig = 0.935534, loss_refine = -0.194798 [Step 19] loss_orig = 0.935540, loss_refine = -0.194616 [Step 19] loss_orig = -0.934430, loss_refine = 1.365484 [Step 19] loss_orig = -0.935043, loss_refine = -0.974782 [Step 19] loss_orig = 0.935503, loss_refine = 1.365587 2%|▏ | 20/1208 [26:00<24:51:34, 75.33s/it] {'loss': 0.0004, 'grad_norm': 6.593131559360708, 'learning_rate': 9.834437086092716e-07, 'completion_length': 117.875, 'rewards/accuracy_reward_action': 0.6666666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.0833333333333335, 'reward_std': 0.6054208079973856, 'kl': 0.00970458984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.13} 2%|▏ | 20/1208 [26:00<24:51:34, 75.33s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 55872: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'invert the lens'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 56745: cache has only 0 modules [Step 20] loss_orig = -0.503482, loss_refine = 1.365352[Step 20] loss_orig = -0.501969, loss_refine = -1.754561[Step 20] loss_orig = -0.503386, loss_refine = 1.365335 [Step 20] loss_orig = 2.184174, loss_refine = -0.194719 [Step 20] loss_orig = -0.503243, loss_refine = -0.194641 [Step 20] loss_orig = 0.840846, loss_refine = -0.194553 [Step 20] loss_orig = -0.503236, loss_refine = -0.194621 [Step 20] loss_orig = -0.503075, loss_refine = -0.194534 Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 57618: cache has only 0 modules 2%|▏ | 21/1208 [27:18<25:03:47, 76.01s/it] {'loss': 0.0004, 'grad_norm': 9.877428375147293, 'learning_rate': 9.826158940397351e-07, 'completion_length': 112.79166666666667, 'rewards/accuracy_reward_action': 0.625, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 0.9166666666666666, 'reward': 1.6666666666666667, 'reward_std': 0.7702379624048868, 'kl': 0.01470947265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.14} 2%|▏ | 21/1208 [27:18<25:03:47, 76.01s/it]Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 58491: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 0.25 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 59364: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Show translate options'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 60237: cache has only 0 modules [Step 21] loss_orig = 0.195414, loss_refine = 0.000159[Step 21] loss_orig = -1.364385, loss_refine = -1.322493 [Step 21] loss_orig = 0.195862, loss_refine = 0.000295 [Step 21] loss_orig = 0.195325, loss_refine = 1.323514[Step 21] loss_orig = -1.364838, loss_refine = -1.321597 [Step 21] loss_orig = 1.755651, loss_refine = 0.000558 [Step 21] loss_orig = 0.195311, loss_refine = 1.323183 [Step 21] loss_orig = 0.195734, loss_refine = 0.000352 2%|▏ | 22/1208 [28:28<24:27:58, 74.27s/it] {'loss': 0.0005, 'grad_norm': 8.40890864296364, 'learning_rate': 9.817880794701985e-07, 'completion_length': 106.45833333333333, 'rewards/accuracy_reward_action': 0.6666666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 1.9583333333333333, 'reward_std': 0.6199029882748922, 'kl': 0.011505126953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.15} 2%|▏ | 22/1208 [28:28<24:27:58, 74.27s/it]Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 61110: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_ArrowCircle_M'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 61983: cache has only 0 modules [Step 22] loss_orig = -0.352183, loss_refine = 1.208575[Step 22] loss_orig = -0.353179, loss_refine = -0.723972[Step 22] loss_orig = -0.352836, loss_refine = -1.689993 [Step 22] loss_orig = 2.477125, loss_refine = 0.242096 [Step 22] loss_orig = -0.352839, loss_refine = -0.723794 [Step 22] loss_orig = -0.353235, loss_refine = 1.208087 [Step 22] loss_orig = -0.352819, loss_refine = 0.242062 [Step 22] loss_orig = -0.353099, loss_refine = 0.241703 Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 62856: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'manage the outlayer'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 63729: cache has only 0 modules [Step 22] loss_orig = 1.208487, loss_refine = 0.840655[Step 22] loss_orig = -0.724218, loss_refine = -0.503673[Step 22] loss_orig = -0.723484, loss_refine = -1.847593[Step 22] loss_orig = -0.723713, loss_refine = 0.840592 [Step 22] loss_orig = -0.723905, loss_refine = -0.503281 [Step 22] loss_orig = 1.207851, loss_refine = 0.840107 [Step 22] loss_orig = 1.207677, loss_refine = 0.840706 [Step 22] loss_orig = -0.723744, loss_refine = -0.503039 2%|▏ | 23/1208 [29:37<23:56:35, 72.74s/it] {'loss': 0.0006, 'grad_norm': 13.016806914751243, 'learning_rate': 9.809602649006623e-07, 'completion_length': 93.40625, 'rewards/accuracy_reward_action': 0.6875, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 1.0, 'reward': 2.09375, 'reward_std': 0.6625561639666557, 'kl': 0.01861572265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.6875, 'epoch': 0.15} 2%|▏ | 23/1208 [29:37<23:56:35, 72.74s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 64602: cache has only 0 modules Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 65475: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open photo'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 66348: cache has only 0 modules [Step 23] loss_orig = -0.352847, loss_refine = 0.725030[Step 23] loss_orig = -0.352959, loss_refine = 0.725412[Step 23] loss_orig = 2.475011, loss_refine = 0.725206 [Step 23] loss_orig = -0.353114, loss_refine = 0.724975 [Step 23] loss_orig = -0.352352, loss_refine = 0.725286 [Step 23] loss_orig = -0.353121, loss_refine = -1.206995[Step 23] loss_orig = -0.353284, loss_refine = -1.206706 [Step 23] loss_orig = -0.353379, loss_refine = -1.206603 2%|▏ | 24/1208 [30:38<22:41:15, 68.98s/it] {'loss': 0.001, 'grad_norm': 5.386403540838747, 'learning_rate': 9.801324503311258e-07, 'completion_length': 86.16666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.4082186420758565, 'kl': 0.023040771484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.16} 2%|▏ | 24/1208 [30:38<22:41:15, 68.98s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 67221: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 68094: cache has only 0 modules 2%|▏ | 25/1208 [31:17<19:44:10, 60.06s/it] {'loss': 0.0008, 'grad_norm': 31.428642845011083, 'learning_rate': 9.793046357615894e-07, 'completion_length': 84.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.6094194948673248, 'kl': 0.02008056640625, 'clip_ratio': 0.0, 'epoch': 0.17} 2%|▏ | 25/1208 [31:17<19:44:10, 60.06s/it]Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 68967: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 69840: cache has only 0 modules 2%|▏ | 26/1208 [32:06<18:39:50, 56.85s/it] {'loss': 0.0007, 'grad_norm': 8.181087658532446, 'learning_rate': 9.784768211920528e-07, 'completion_length': 92.4375, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.3125, 'reward_std': 0.6943258494138718, 'kl': 0.01812744140625, 'clip_ratio': 0.0, 'epoch': 0.17} 2%|▏ | 26/1208 [32:06<18:39:50, 56.85s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 70713: cache has only 0 modules Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 71586: cache has only 0 modules 2%|▏ | 27/1208 [33:04<18:44:15, 57.12s/it] {'loss': 0.0011, 'grad_norm': 5.107217786323231, 'learning_rate': 9.776490066225166e-07, 'completion_length': 112.5, 'rewards/accuracy_reward_action': 0.6875, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.875, 'reward': 1.9375, 'reward_std': 1.0805450081825256, 'kl': 0.0281982421875, 'clip_ratio': 0.0, 'epoch': 0.18} 2%|▏ | 27/1208 [33:04<18:44:15, 57.12s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 72459: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 73332: cache has only 0 modules [Step 27] loss_orig = -0.724009, loss_refine = -0.660193[Step 27] loss_orig = 1.208091, loss_refine = 0.662089 [Step 27] loss_orig = -0.724141, loss_refine = -0.661118[Step 27] loss_orig = -0.723650, loss_refine = -0.659435 [Step 27] loss_orig = 1.207703, loss_refine = 1.984311[Step 27] loss_orig = -0.723975, loss_refine = 0.661961 [Step 27] loss_orig = -0.724038, loss_refine = -0.660766[Step 27] loss_orig = 1.208005, loss_refine = -0.660699 Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 74205: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxdiff coord reward errorcloser to gt boxcloser to gt box diff coord reward error closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 75078: cache has only 0 modules [Step 27] loss_orig = -0.934742, loss_refine = -1.246524[Step 27] loss_orig = -0.932927, loss_refine = 0.288132 [Step 27] loss_orig = 0.935715, loss_refine = 1.056352 [Step 27] loss_orig = -0.934978, loss_refine = -0.478980 [Step 27] loss_orig = 0.935763, loss_refine = -0.479199 [Step 27] loss_orig = 0.935472, loss_refine = 1.056481 [Step 27] loss_orig = 0.935744, loss_refine = -1.246841 [Step 27] loss_orig = -0.934881, loss_refine = 1.055929 2%|▏ | 28/1208 [34:26<21:08:01, 64.48s/it] {'loss': 0.0007, 'grad_norm': 28.732516959152896, 'learning_rate': 9.768211920529801e-07, 'completion_length': 105.21875, 'rewards/accuracy_reward_action': 0.53125, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.875, 'reward': 1.5, 'reward_std': 0.7776176929473877, 'kl': 0.0142822265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.1875, 'epoch': 0.19} 2%|▏ | 28/1208 [34:26<21:08:01, 64.48s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 0.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 75951: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open settings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.125 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 76824: cache has only 0 modules [Step 28] loss_orig = -0.353000, loss_refine = 0.215195[Step 28] loss_orig = -0.353255, loss_refine = 0.214878[Step 28] loss_orig = -0.352984, loss_refine = 1.073862 [Step 28] loss_orig = 2.474662, loss_refine = -2.359240 [Step 28] loss_orig = -0.352888, loss_refine = 0.214857 [Step 28] loss_orig = -0.352818, loss_refine = 0.215544 [Step 28] loss_orig = -0.352699, loss_refine = 0.215015 [Step 28] loss_orig = -0.353097, loss_refine = 0.215207 Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 77697: cache has only 0 modules 2%|▏ | 29/1208 [35:31<21:10:03, 64.63s/it] {'loss': 0.0009, 'grad_norm': 9.648165685320054, 'learning_rate': 9.759933774834437e-07, 'completion_length': 97.04166666666667, 'rewards/accuracy_reward_action': 0.3333333333333333, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9166666666666666, 'reward': 1.5833333333333333, 'reward_std': 0.7541806201140085, 'kl': 0.02020263671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 0.19} 2%|▏ | 29/1208 [35:31<21:10:03, 64.63s/it]Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 78570: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'use airplay'. Your previous answer was [{'action': 'click', 'coordinate': [686, 2220]}, {'action': 'click', 'coordinate': [1012, 2220]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box diff coord reward error closer to gt box diff coord reward error closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.625 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 79443: cache has only 0 modules [Step 29] loss_orig = 0.724871, loss_refine = -1.321398 [Step 29] loss_orig = 0.725137, loss_refine = 0.000343 [Step 29] loss_orig = -1.206973, loss_refine = 0.000672 [Step 29] loss_orig = -1.206970, loss_refine = 0.000376[Step 29] loss_orig = 0.725194, loss_refine = 0.000692 [Step 29] loss_orig = 0.724787, loss_refine = 1.323686[Step 29] loss_orig = 0.725194, loss_refine = -1.322538 [Step 29] loss_orig = -1.206189, loss_refine = 1.323276 Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 80316: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft search'. Your previous answer was [{'action': 'click', 'coordinate': [1327, 159]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 81189: cache has only 0 modules [Step 29] loss_orig = 1.208361, loss_refine = -0.352798 [Step 29] loss_orig = 1.207929, loss_refine = -0.352892 [Step 29] loss_orig = -0.723604, loss_refine = -0.353255[Step 29] loss_orig = -0.723150, loss_refine = -0.353216 [Step 29] loss_orig = -0.723832, loss_refine = -0.352933 [Step 29] loss_orig = 1.208188, loss_refine = -0.352980 [Step 29] loss_orig = -0.723285, loss_refine = -0.353185 [Step 29] loss_orig = -0.723631, loss_refine = 2.474792 2%|▏ | 30/1208 [37:00<23:33:49, 72.01s/it] {'loss': 0.0005, 'grad_norm': 10.64715549868127, 'learning_rate': 9.751655629139073e-07, 'completion_length': 123.5, 'rewards/accuracy_reward_action': 0.5625, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.875, 'reward': 1.4375, 'reward_std': 0.6245335042476654, 'kl': 0.018798828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 0.2} 2%|▏ | 30/1208 [37:00<23:33:49, 72.01s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 82062: cache has only 0 modules Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 82935: cache has only 0 modules 3%|▎ | 31/1208 [37:42<20:37:29, 63.08s/it] {'loss': 0.0009, 'grad_norm': 5.547815044823038, 'learning_rate': 9.743377483443708e-07, 'completion_length': 103.6875, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.6924468874931335, 'kl': 0.022705078125, 'clip_ratio': 0.0, 'epoch': 0.21} 3%|▎ | 31/1208 [37:42<20:37:29, 63.08s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 83808: cache has only 0 modules Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 84681: cache has only 0 modules 3%|▎ | 32/1208 [38:25<18:36:30, 56.96s/it] {'loss': 0.0005, 'grad_norm': 10.941651067943459, 'learning_rate': 9.735099337748344e-07, 'completion_length': 99.0625, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.7315178513526917, 'kl': 0.013092041015625, 'clip_ratio': 0.0, 'epoch': 0.21} 3%|▎ | 32/1208 [38:25<18:36:30, 56.96s/it]Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 85554: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 86427: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 87300: cache has only 0 modules [Step 32] loss_orig = -0.352947, loss_refine = 1.208575[Step 32] loss_orig = -0.352945, loss_refine = -0.723156[Step 32] loss_orig = -0.353056, loss_refine = 1.207798[Step 32] loss_orig = -0.352525, loss_refine = -0.723545[Step 32] loss_orig = -0.352670, loss_refine = -0.723310[Step 32] loss_orig = -0.352546, loss_refine = -0.724204[Step 32] loss_orig = -0.352913, loss_refine = 1.207858 [Step 32] loss_orig = 2.475106, loss_refine = -0.724059 3%|▎ | 33/1208 [39:14<17:50:35, 54.67s/it] {'loss': 0.0011, 'grad_norm': 7.506460341655114, 'learning_rate': 9.72682119205298e-07, 'completion_length': 99.79166666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.2083333333333335, 'reward_std': 0.5039908389250437, 'kl': 0.02740478515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.22} 3%|▎ | 33/1208 [39:14<17:50:35, 54.67s/it]Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 88173: cache has only 0 modules Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 89046: cache has only 0 modules 3%|▎ | 34/1208 [39:55<16:30:04, 50.60s/it] {'loss': 0.001, 'grad_norm': 15.913877642604648, 'learning_rate': 9.718543046357615e-07, 'completion_length': 89.4375, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.6208146214485168, 'kl': 0.02593994140625, 'clip_ratio': 0.0, 'epoch': 0.23} 3%|▎ | 34/1208 [39:55<16:30:04, 50.60s/it]Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 89919: cache has only 0 modules Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 90792: cache has only 0 modules ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Split screen'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 91665: cache has only 0 modules [Step 34] loss_orig = 1.620619, loss_refine = -0.584458 [Step 34] loss_orig = -0.539016, loss_refine = -0.588602 [Step 34] loss_orig = -0.538476, loss_refine = 0.354279 [Step 34] loss_orig = 1.620653, loss_refine = -0.588583[Step 34] loss_orig = -0.538495, loss_refine = 0.353975 [Step 34] loss_orig = -0.539044, loss_refine = -0.588826[Step 34] loss_orig = -0.538794, loss_refine = 2.239111 [Step 34] loss_orig = -0.539156, loss_refine = -0.588880 3%|▎ | 35/1208 [41:05<18:20:34, 56.30s/it] {'loss': 0.001, 'grad_norm': 7.219332230759481, 'learning_rate': 9.710264900662251e-07, 'completion_length': 110.0, 'rewards/accuracy_reward_action': 0.8333333333333334, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.0416666666666665, 'reward_std': 0.6860308845837911, 'kl': 0.02587890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.23} 3%|▎ | 35/1208 [41:05<18:20:34, 56.30s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 92538: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 93411: cache has only 0 modules 3%|▎ | 36/1208 [41:48<17:03:28, 52.40s/it] {'loss': 0.0008, 'grad_norm': 6.820715221652341, 'learning_rate': 9.701986754966887e-07, 'completion_length': 106.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.01885986328125, 'clip_ratio': 0.0, 'epoch': 0.24} 3%|▎ | 36/1208 [41:48<17:03:28, 52.40s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 94284: cache has only 0 modules Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 95157: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 96030: cache has only 0 modules [Step 36] loss_orig = -0.351246, loss_refine = 0.000547[Step 36] loss_orig = -0.352628, loss_refine = 0.000372 [Step 36] loss_orig = -0.352041, loss_refine = 0.000392 [Step 36] loss_orig = -0.352834, loss_refine = 0.000512[Step 36] loss_orig = -0.353124, loss_refine = 0.000674 [Step 36] loss_orig = -0.353086, loss_refine = 0.000230 [Step 36] loss_orig = 2.475142, loss_refine = 0.000253 [Step 36] loss_orig = -0.353031, loss_refine = 0.001020 3%|▎ | 37/1208 [42:56<18:33:42, 57.06s/it] {'loss': 0.0041, 'grad_norm': 4.691393438993866, 'learning_rate': 9.693708609271523e-07, 'completion_length': 101.66666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.5833333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.10736083984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.25} 3%|▎ | 37/1208 [42:56<18:33:42, 57.06s/it]Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 96903: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 97776: cache has only 0 modules [Step 37] loss_orig = -0.352728, loss_refine = -0.539585 [Step 37] loss_orig = -0.352875, loss_refine = -0.538896 [Step 37] loss_orig = -0.352971, loss_refine = 1.620364 [Step 37] loss_orig = 2.474720, loss_refine = -0.539501[Step 37] loss_orig = -0.353297, loss_refine = 1.620572 [Step 37] loss_orig = -0.353014, loss_refine = -0.539353 [Step 37] loss_orig = -0.353162, loss_refine = -0.539523 [Step 37] loss_orig = -0.351501, loss_refine = -0.539423 Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 98649: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Conditional Formatting'. Your previous answer was [{'action': 'click', 'coordinate': [1004, 98]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 99522: cache has only 0 modules [Step 37] loss_orig = 0.000504, loss_refine = 0.725263[Step 37] loss_orig = 0.000466, loss_refine = -1.206765[Step 37] loss_orig = 0.000339, loss_refine = 0.725112[Step 37] loss_orig = 0.000190, loss_refine = 0.724956[Step 37] loss_orig = 0.000476, loss_refine = -1.207115 [Step 37] loss_orig = 0.000462, loss_refine = 0.725137[Step 37] loss_orig = 0.000445, loss_refine = -1.206823 [Step 37] loss_orig = 0.000405, loss_refine = 0.724779 3%|▎ | 38/1208 [44:05<19:44:01, 60.72s/it] {'loss': 0.0006, 'grad_norm': 8.10413560249746, 'learning_rate': 9.685430463576158e-07, 'completion_length': 92.75, 'rewards/accuracy_reward_action': 0.90625, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.3335031494498253, 'kl': 0.013214111328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.6875, 'epoch': 0.25} 3%|▎ | 38/1208 [44:05<19:44:01, 60.72s/it]Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 100395: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Font Name'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 101268: cache has only 0 modules [Step 38] loss_orig = 1.207749, loss_refine = -1.364874 [Step 38] loss_orig = 1.207889, loss_refine = 1.756513[Step 38] loss_orig = -0.724103, loss_refine = -1.364580[Step 38] loss_orig = -0.724027, loss_refine = 0.195770[Step 38] loss_orig = 1.208246, loss_refine = 0.195378 [Step 38] loss_orig = -0.723930, loss_refine = 0.195328 [Step 38] loss_orig = -0.724072, loss_refine = 0.195286 [Step 38] loss_orig = -0.724040, loss_refine = 0.195567 Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 102141: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 103014: cache has only 0 modules [Step 38] loss_orig = 0.000692, loss_refine = -2.159557 [Step 38] loss_orig = 0.001441, loss_refine = 0.000188[Step 38] loss_orig = 0.000862, loss_refine = 0.000575 [Step 38] loss_orig = 0.000847, loss_refine = 1.080804 [Step 38] loss_orig = 0.000480, loss_refine = 1.080585 [Step 38] loss_orig = 0.000587, loss_refine = 0.000296[Step 38] loss_orig = 0.000459, loss_refine = 0.000378[Step 38] loss_orig = 0.000545, loss_refine = 0.000569 3%|▎ | 39/1208 [45:31<22:11:52, 68.36s/it] {'loss': 0.0005, 'grad_norm': 32.99297373334702, 'learning_rate': 9.677152317880794e-07, 'completion_length': 103.375, 'rewards/accuracy_reward_action': 0.84375, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.96875, 'reward': 1.9375, 'reward_std': 0.5210598111152649, 'kl': 0.0150146484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.1875, 'epoch': 0.26} 3%|▎ | 39/1208 [45:32<22:11:52, 68.36s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 103887: cache has only 0 modules Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 0.5 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 104760: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Subscript'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 105633: cache has only 0 modules [Step 39] loss_orig = -0.934136, loss_refine = -0.539332 [Step 39] loss_orig = -0.934334, loss_refine = -0.539112[Step 39] loss_orig = 0.935915, loss_refine = 1.620624 [Step 39] loss_orig = 0.936352, loss_refine = -0.539499[Step 39] loss_orig = -0.934236, loss_refine = -0.539170 [Step 39] loss_orig = 0.938109, loss_refine = -0.539258 [Step 39] loss_orig = 0.935948, loss_refine = -0.538808[Step 39] loss_orig = -0.934735, loss_refine = 1.622986 3%|▎ | 40/1208 [46:41<22:17:37, 68.71s/it] {'loss': 0.0009, 'grad_norm': 8.847495439304225, 'learning_rate': 9.66887417218543e-07, 'completion_length': 99.20833333333333, 'rewards/accuracy_reward_action': 0.7083333333333334, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.0833333333333335, 'reward_std': 0.5106516679128011, 'kl': 0.0230712890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.26} 3%|▎ | 40/1208 [46:41<22:17:37, 68.71s/it]Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 106506: cache has only 0 modules Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 107379: cache has only 0 modules 3%|▎ | 41/1208 [47:18<19:09:54, 59.12s/it] {'loss': 0.0004, 'grad_norm': 11.977238823128731, 'learning_rate': 9.660596026490065e-07, 'completion_length': 90.5, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.7301712930202484, 'kl': 0.010955810546875, 'clip_ratio': 0.0, 'epoch': 0.27} 3%|▎ | 41/1208 [47:18<19:09:54, 59.12s/it]Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 108252: cache has only 0 modules Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 109125: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Google Images'. Your previous answer was [{'action': 'click', 'coordinate': [907, 206]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 109998: cache has only 0 modules [Step 41] loss_orig = 0.000319, loss_refine = 0.724909 [Step 41] loss_orig = 0.000357, loss_refine = -1.207023[Step 41] loss_orig = 0.000463, loss_refine = 0.724984 [Step 41] loss_orig = 0.000387, loss_refine = 0.724866 [Step 41] loss_orig = 0.000394, loss_refine = 0.724703[Step 41] loss_orig = 0.000675, loss_refine = -1.206927 [Step 41] loss_orig = 0.000584, loss_refine = -1.206909[Step 41] loss_orig = 0.000306, loss_refine = 0.724804 3%|▎ | 42/1208 [48:10<18:30:45, 57.16s/it] {'loss': 0.0004, 'grad_norm': 7.982014710293197, 'learning_rate': 9.652317880794701e-07, 'completion_length': 98.66666666666667, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.0833333333333335, 'reward_std': 0.4506907065709432, 'kl': 0.010955810546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.28} 3%|▎ | 42/1208 [48:10<18:30:45, 57.16s/it]Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 110871: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Search by image'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 111744: cache has only 0 modules [Step 42] loss_orig = -0.353004, loss_refine = -1.983060[Step 42] loss_orig = -0.353029, loss_refine = 0.661514 [Step 42] loss_orig = -0.352902, loss_refine = 0.662490 [Step 42] loss_orig = -0.352381, loss_refine = 0.661739 [Step 42] loss_orig = -0.353201, loss_refine = -0.660056 [Step 42] loss_orig = -0.353148, loss_refine = -0.660504 [Step 42] loss_orig = -0.353115, loss_refine = 0.662092 [Step 42] loss_orig = 2.474565, loss_refine = 0.661981 Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 112617: cache has only 0 modules 4%|▎ | 43/1208 [49:08<18:32:31, 57.30s/it] {'loss': 0.0007, 'grad_norm': 9.005090556712158, 'learning_rate': 9.644039735099337e-07, 'completion_length': 91.16666666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.524130791425705, 'kl': 0.012969970703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.28} 4%|▎ | 43/1208 [49:08<18:32:31, 57.30s/it]Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 113490: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 114363: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt box closer to gt boxcloser to gt box diff coord reward error closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 115236: cache has only 0 modules [Step 43] loss_orig = -0.503170, loss_refine = -0.835989[Step 43] loss_orig = -0.503061, loss_refine = 0.505358 [Step 43] loss_orig = 0.840878, loss_refine = 1.853558 [Step 43] loss_orig = -0.503330, loss_refine = -0.839181[Step 43] loss_orig = -0.503230, loss_refine = 0.504899 [Step 43] loss_orig = -0.503263, loss_refine = -0.838966 [Step 43] loss_orig = 2.184216, loss_refine = -0.839156 [Step 43] loss_orig = -0.502505, loss_refine = 0.504727 4%|▎ | 44/1208 [50:29<20:49:41, 64.42s/it] {'loss': 0.0015, 'grad_norm': 7.951268522199704, 'learning_rate': 9.635761589403972e-07, 'completion_length': 118.0, 'rewards/accuracy_reward_action': 0.8333333333333334, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.0416666666666665, 'reward_std': 0.7096391916275024, 'kl': 0.02325439453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.29} 4%|▎ | 44/1208 [50:29<20:49:41, 64.42s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 116109: cache has only 0 modules Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 116982: cache has only 0 modules 4%|▎ | 45/1208 [51:10<18:31:04, 57.32s/it] {'loss': 0.0007, 'grad_norm': 18.632074716595913, 'learning_rate': 9.627483443708608e-07, 'completion_length': 97.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.016815185546875, 'clip_ratio': 0.0, 'epoch': 0.3} 4%|▎ | 45/1208 [51:10<18:31:04, 57.32s/it]Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 117855: cache has only 0 modules Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 118728: cache has only 0 modules 4%|▍ | 46/1208 [52:02<18:02:03, 55.87s/it] {'loss': 0.0008, 'grad_norm': 10.761286876369883, 'learning_rate': 9.619205298013244e-07, 'completion_length': 99.1875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.6307864785194397, 'kl': 0.0196533203125, 'clip_ratio': 0.0, 'epoch': 0.3} 4%|▍ | 46/1208 [52:02<18:02:03, 55.87s/it]Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 119601: cache has only 0 modules Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 120474: cache has only 0 modules 4%|▍ | 47/1208 [52:41<16:19:32, 50.62s/it] {'loss': 0.0007, 'grad_norm': 10.571782779899662, 'learning_rate': 9.61092715231788e-07, 'completion_length': 87.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.0166015625, 'clip_ratio': 0.0, 'epoch': 0.31} 4%|▍ | 47/1208 [52:41<16:19:32, 50.62s/it]Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 121347: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 122220: cache has only 0 modules 4%|▍ | 48/1208 [53:13<14:32:02, 45.11s/it] {'loss': 0.0007, 'grad_norm': 6.460588896082171, 'learning_rate': 9.602649006622515e-07, 'completion_length': 88.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.017333984375, 'clip_ratio': 0.0, 'epoch': 0.32} 4%|▍ | 48/1208 [53:13<14:32:02, 45.11s/it]Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 123093: cache has only 0 modules Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 123966: cache has only 0 modules 4%|▍ | 49/1208 [53:51<13:52:30, 43.10s/it] {'loss': 0.0006, 'grad_norm': 7.310543351187007, 'learning_rate': 9.594370860927153e-07, 'completion_length': 89.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.015380859375, 'clip_ratio': 0.0, 'epoch': 0.32} 4%|▍ | 49/1208 [53:51<13:52:30, 43.10s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 124839: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Format'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 125712: cache has only 0 modules [Step 49] loss_orig = 0.001275, loss_refine = -1.347456 [Step 49] loss_orig = 0.001023, loss_refine = 1.049170 [Step 49] loss_orig = 0.000484, loss_refine = 1.048795 [Step 49] loss_orig = 0.001113, loss_refine = -0.149398 [Step 49] loss_orig = 0.000777, loss_refine = -0.148719[Step 49] loss_orig = 0.000550, loss_refine = -0.149009 [Step 49] loss_orig = 0.000644, loss_refine = 1.049492 [Step 49] loss_orig = 0.001520, loss_refine = -1.347305 Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 126585: cache has only 0 modules 4%|▍ | 50/1208 [54:53<15:38:48, 48.64s/it] {'loss': 0.001, 'grad_norm': 18.854966344637972, 'learning_rate': 9.586092715231787e-07, 'completion_length': 102.25, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.4324776728947957, 'kl': 0.02764892578125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 0.33} 4%|▍ | 50/1208 [54:53<15:38:48, 48.64s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 127458: cache has only 0 modules Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 128331: cache has only 0 modules 4%|▍ | 51/1208 [55:28<14:22:16, 44.72s/it] {'loss': 0.0007, 'grad_norm': 70.07755961051778, 'learning_rate': 9.577814569536422e-07, 'completion_length': 100.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.6034669280052185, 'kl': 0.017547607421875, 'clip_ratio': 0.0, 'epoch': 0.34} 4%|▍ | 51/1208 [55:28<14:22:16, 44.72s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 129204: cache has only 0 modules Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 130077: cache has only 0 modules 4%|▍ | 52/1208 [56:06<13:41:01, 42.61s/it] {'loss': 0.0009, 'grad_norm': 7.0216535458347735, 'learning_rate': 9.56953642384106e-07, 'completion_length': 95.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.02142333984375, 'clip_ratio': 0.0, 'epoch': 0.34} 4%|▍ | 52/1208 [56:06<13:41:01, 42.61s/it]Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 130950: cache has only 0 modules Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 131823: cache has only 0 modules 4%|▍ | 53/1208 [56:44<13:13:36, 41.23s/it] {'loss': 0.0007, 'grad_norm': 41.26862532193993, 'learning_rate': 9.561258278145696e-07, 'completion_length': 90.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.018646240234375, 'clip_ratio': 0.0, 'epoch': 0.35} 4%|▍ | 53/1208 [56:44<13:13:36, 41.23s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 132696: cache has only 0 modules Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 133569: cache has only 0 modules 4%|▍ | 54/1208 [57:31<13:48:22, 43.07s/it] {'loss': 0.001, 'grad_norm': 3.8549897351007023, 'learning_rate': 9.552980132450332e-07, 'completion_length': 94.8125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.0238037109375, 'clip_ratio': 0.0, 'epoch': 0.36} 4%|▍ | 54/1208 [57:31<13:48:22, 43.07s/it]Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 134442: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element 343'. Your previous answer was [{'action': 'click', 'coordinate': [2009, 1325]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 135315: cache has only 0 modules [Step 54] loss_orig = -0.352888, loss_refine = -1.618888 [Step 54] loss_orig = 2.475980, loss_refine = 0.540971 [Step 54] loss_orig = -0.352926, loss_refine = 0.540645 [Step 54] loss_orig = -0.352332, loss_refine = 0.540786 [Step 54] loss_orig = -0.352784, loss_refine = 0.540572 [Step 54] loss_orig = -0.352261, loss_refine = 0.540645 [Step 54] loss_orig = -0.351418, loss_refine = -1.619164 [Step 54] loss_orig = -0.352886, loss_refine = 0.540472 Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 136188: cache has only 0 modules 5%|▍ | 55/1208 [58:23<14:34:01, 45.48s/it] {'loss': 0.0007, 'grad_norm': 10.469474256301915, 'learning_rate': 9.544701986754965e-07, 'completion_length': 84.83333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.39000560839970905, 'kl': 0.02239990234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.36} 5%|▍ | 55/1208 [58:23<14:34:01, 45.48s/it]Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 137061: cache has only 0 modules Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 137934: cache has only 0 modules 5%|▍ | 56/1208 [59:03<14:01:41, 43.84s/it] {'loss': 0.0049, 'grad_norm': 10.297686567071628, 'learning_rate': 9.536423841059602e-07, 'completion_length': 100.875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.636739045381546, 'kl': 0.1240234375, 'clip_ratio': 0.0, 'epoch': 0.37} 5%|▍ | 56/1208 [59:03<14:01:41, 43.84s/it]Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 138807: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 139680: cache has only 0 modules [Step 56] loss_orig = 2.475350, loss_refine = -0.352008 [Step 56] loss_orig = -0.351809, loss_refine = -0.351871 [Step 56] loss_orig = -0.352686, loss_refine = -0.351888 [Step 56] loss_orig = -0.351595, loss_refine = -0.352144[Step 56] loss_orig = -0.352061, loss_refine = 2.474887 [Step 56] loss_orig = -0.350734, loss_refine = -0.352578 [Step 56] loss_orig = -0.351451, loss_refine = -0.352498 [Step 56] loss_orig = -0.352193, loss_refine = -0.351855 Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 140553: cache has only 0 modules 5%|▍ | 57/1208 [1:00:07<15:59:03, 49.99s/it] {'loss': 0.0013, 'grad_norm': 10.009157974330051, 'learning_rate': 9.528145695364238e-07, 'completion_length': 103.91666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.3535533845424652, 'kl': 0.035888671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 0.38} 5%|▍ | 57/1208 [1:00:07<15:59:03, 49.99s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 141426: cache has only 0 modules Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 142299: cache has only 0 modules 5%|▍ | 58/1208 [1:00:45<14:51:05, 46.49s/it] {'loss': 0.0011, 'grad_norm': 8.077956917599787, 'learning_rate': 9.519867549668874e-07, 'completion_length': 92.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.02874755859375, 'clip_ratio': 0.0, 'epoch': 0.38} 5%|▍ | 58/1208 [1:00:45<14:51:05, 46.49s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 143172: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 144045: cache has only 0 modules 5%|▍ | 59/1208 [1:01:29<14:31:51, 45.53s/it] {'loss': 0.0014, 'grad_norm': 12.348679805426976, 'learning_rate': 9.511589403973509e-07, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.49022960662841797, 'kl': 0.03564453125, 'clip_ratio': 0.0, 'epoch': 0.39} 5%|▍ | 59/1208 [1:01:29<14:31:51, 45.53s/it]Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 144918: cache has only 0 modules Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 145791: cache has only 0 modules 5%|▍ | 60/1208 [1:02:07<13:48:07, 43.28s/it] {'loss': 0.0014, 'grad_norm': 8.868008580204947, 'learning_rate': 9.503311258278145e-07, 'completion_length': 87.375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.0625, 'reward_std': 0.44403792917728424, 'kl': 0.033966064453125, 'clip_ratio': 0.0, 'epoch': 0.4} 5%|▍ | 60/1208 [1:02:07<13:48:07, 43.28s/it]Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 146664: cache has only 0 modules Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 147537: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sky Blue Bikes'. Your previous answer was [ {'action': 'click', 'coordinate': [333, 466] } ] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 148410: cache has only 0 modules [Step 60] loss_orig = 0.000865, loss_refine = -0.281297[Step 60] loss_orig = 0.000992, loss_refine = -0.280974[Step 60] loss_orig = 0.002282, loss_refine = -0.281188 [Step 60] loss_orig = 0.000880, loss_refine = -1.409073 [Step 60] loss_orig = 0.003563, loss_refine = -0.281035 [Step 60] loss_orig = 0.000665, loss_refine = -0.281141 [Step 60] loss_orig = 0.000799, loss_refine = 0.847449 [Step 60] loss_orig = 0.001161, loss_refine = 1.975294 5%|▌ | 61/1208 [1:03:01<14:51:02, 46.61s/it] {'loss': 0.0012, 'grad_norm': 8.92331572378303, 'learning_rate': 9.49503311258278e-07, 'completion_length': 105.875, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 0.9166666666666666, 'reward': 1.9583333333333333, 'reward_std': 0.5736427307128906, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 0.4} 5%|▌ | 61/1208 [1:03:01<14:51:02, 46.61s/it]Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 149283: cache has only 0 modules Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 150156: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open gmail'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxdiff coord reward error closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.375 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 151029: cache has only 0 modules [Step 61] loss_orig = 0.410204, loss_refine = 0.767631[Step 61] loss_orig = 1.501109, loss_refine = -1.525874 [Step 61] loss_orig = -0.680773, loss_refine = 0.765539 [Step 61] loss_orig = -0.681583, loss_refine = 0.001310[Step 61] loss_orig = -0.681138, loss_refine = 0.773147 [Step 61] loss_orig = -0.681189, loss_refine = 0.764759 [Step 61] loss_orig = -0.681503, loss_refine = 0.001790 [Step 61] loss_orig = 1.501576, loss_refine = -1.526335 5%|▌ | 62/1208 [1:03:59<15:55:29, 50.03s/it] {'loss': 0.0019, 'grad_norm': 6.648643922490396, 'learning_rate': 9.486754966887417e-07, 'completion_length': 107.5, 'rewards/accuracy_reward_action': 0.6666666666666666, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 0.9166666666666666, 'reward': 1.8333333333333333, 'reward_std': 0.8596620460351309, 'kl': 0.023681640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.41} 5%|▌ | 62/1208 [1:03:59<15:55:29, 50.03s/it]Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 151902: cache has only 0 modules Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 152775: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functional icon'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 153648: cache has only 0 modules [Step 62] loss_orig = 0.001008, loss_refine = 0.541471[Step 62] loss_orig = 0.001326, loss_refine = 1.620814 [Step 62] loss_orig = 0.001665, loss_refine = 0.541444[Step 62] loss_orig = 0.000728, loss_refine = -0.538597 [Step 62] loss_orig = 0.000967, loss_refine = -0.539017 [Step 62] loss_orig = 0.002127, loss_refine = -1.619245 [Step 62] loss_orig = 0.000898, loss_refine = -0.539218 [Step 62] loss_orig = 0.000906, loss_refine = 0.540909 5%|▌ | 63/1208 [1:04:44<15:26:10, 48.53s/it] {'loss': 0.0011, 'grad_norm': 8.596936730768565, 'learning_rate': 9.478476821192053e-07, 'completion_length': 86.625, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4629100561141968, 'kl': 0.02880859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.42} 5%|▌ | 63/1208 [1:04:44<15:26:10, 48.53s/it]Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 154521: cache has only 0 modules Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 155394: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 156267: cache has only 0 modules [Step 63] loss_orig = 0.001512, loss_refine = -0.659341 [Step 63] loss_orig = 0.001136, loss_refine = 0.662798[Step 63] loss_orig = 0.001330, loss_refine = -0.659816 [Step 63] loss_orig = 0.001087, loss_refine = -0.659198 [Step 63] loss_orig = 0.001444, loss_refine = 0.662751 [Step 63] loss_orig = 0.000919, loss_refine = -0.660663 [Step 63] loss_orig = 0.001217, loss_refine = -0.660526 [Step 63] loss_orig = 0.001756, loss_refine = 1.986005 5%|▌ | 64/1208 [1:05:48<16:54:51, 53.23s/it] {'loss': 0.0013, 'grad_norm': 6.167381560772956, 'learning_rate': 9.470198675496688e-07, 'completion_length': 101.875, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.125, 'reward_std': 0.36982743938763935, 'kl': 0.0301513671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 0.42} 5%|▌ | 64/1208 [1:05:48<16:54:51, 53.23s/it]Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 157140: cache has only 0 modules Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 158013: cache has only 0 modules 5%|▌ | 65/1208 [1:06:22<15:02:57, 47.40s/it] {'loss': 0.001, 'grad_norm': 13.108888889665145, 'learning_rate': 9.461920529801324e-07, 'completion_length': 83.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4355512708425522, 'kl': 0.0242919921875, 'clip_ratio': 0.0, 'epoch': 0.43} 5%|▌ | 65/1208 [1:06:22<15:02:57, 47.40s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 158886: cache has only 0 modules Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 159759: cache has only 0 modules 5%|▌ | 66/1208 [1:06:57<13:49:32, 43.58s/it] {'loss': 0.0009, 'grad_norm': 4.333124277171203, 'learning_rate': 9.45364238410596e-07, 'completion_length': 85.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.02294921875, 'clip_ratio': 0.0, 'epoch': 0.44} 5%|▌ | 66/1208 [1:06:57<13:49:32, 43.58s/it]Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 160632: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 161505: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Channel watermark'. Your previous answer was [{'action': 'click', 'coordinate': [546, 136]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 162378: cache has only 0 modules [Step 66] loss_orig = 0.003136, loss_refine = -0.539525 [Step 66] loss_orig = 0.000632, loss_refine = -0.538632[Step 66] loss_orig = 0.001083, loss_refine = -0.538381 [Step 66] loss_orig = 0.001148, loss_refine = -0.538858[Step 66] loss_orig = 0.001658, loss_refine = 1.620895[Step 66] loss_orig = 0.002256, loss_refine = -0.539411 [Step 66] loss_orig = 0.001209, loss_refine = 1.620677[Step 66] loss_orig = 0.001408, loss_refine = -0.538825 6%|▌ | 67/1208 [1:07:44<14:10:58, 44.75s/it] {'loss': 0.0009, 'grad_norm': 7.4362899136148615, 'learning_rate': 9.445364238410596e-07, 'completion_length': 96.16666666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.1666666666666665, 'reward_std': 0.30860670407613117, 'kl': 0.0284423828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 0.44} 6%|▌ | 67/1208 [1:07:44<14:10:58, 44.75s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 163251: cache has only 0 modules Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 164124: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command '1'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 164997: cache has only 0 modules [Step 67] loss_orig = -0.352940, loss_refine = 0.126589[Step 67] loss_orig = -0.352965, loss_refine = 0.127148 [Step 67] loss_orig = -0.352467, loss_refine = 0.127150 [Step 67] loss_orig = -0.352434, loss_refine = -0.880950[Step 67] loss_orig = 2.477994, loss_refine = -0.881388 [Step 67] loss_orig = -0.352542, loss_refine = -0.882014 [Step 67] loss_orig = -0.352838, loss_refine = 0.127059 [Step 67] loss_orig = -0.352150, loss_refine = 2.144800 6%|▌ | 68/1208 [1:08:57<16:51:41, 53.25s/it] {'loss': 0.0018, 'grad_norm': 6.448689480646048, 'learning_rate': 9.437086092715231e-07, 'completion_length': 121.125, 'rewards/accuracy_reward_action': 0.7916666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.1666666666666665, 'reward_std': 0.756801575422287, 'kl': 0.0462646484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.45} 6%|▌ | 68/1208 [1:08:57<16:51:41, 53.25s/it]Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 165870: cache has only 0 modules Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 166743: cache has only 0 modules 6%|▌ | 69/1208 [1:09:39<15:45:12, 49.79s/it] {'loss': 0.0011, 'grad_norm': 5.2480191282481705, 'learning_rate': 9.428807947019867e-07, 'completion_length': 98.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.0263671875, 'clip_ratio': 0.0, 'epoch': 0.46} 6%|▌ | 69/1208 [1:09:39<15:45:12, 49.79s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 167616: cache has only 0 modules Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 168489: cache has only 0 modules 6%|▌ | 70/1208 [1:10:11<14:05:38, 44.59s/it] {'loss': 0.0009, 'grad_norm': 15.241735253592143, 'learning_rate': 9.420529801324503e-07, 'completion_length': 81.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.44403792917728424, 'kl': 0.023193359375, 'clip_ratio': 0.0, 'epoch': 0.46} 6%|▌ | 70/1208 [1:10:11<14:05:38, 44.59s/it]Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 169362: cache has only 0 modules Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 170235: cache has only 0 modules 6%|▌ | 71/1208 [1:10:47<13:15:48, 42.00s/it] {'loss': 0.0015, 'grad_norm': 8.528565763180973, 'learning_rate': 9.412251655629139e-07, 'completion_length': 77.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.5260358154773712, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 0.47} 6%|▌ | 71/1208 [1:10:47<13:15:48, 42.00s/it]Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 171108: cache has only 0 modules Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 171981: cache has only 0 modules 6%|▌ | 72/1208 [1:11:36<13:55:02, 44.10s/it] {'loss': 0.0011, 'grad_norm': 11.46680539085298, 'learning_rate': 9.403973509933774e-07, 'completion_length': 103.375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9375, 'reward': 2.125, 'reward_std': 0.6943650841712952, 'kl': 0.0286865234375, 'clip_ratio': 0.0, 'epoch': 0.48} 6%|▌ | 72/1208 [1:11:36<13:55:02, 44.10s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 172854: cache has only 0 modules Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 173727: cache has only 0 modules 6%|▌ | 73/1208 [1:12:11<13:02:06, 41.35s/it] {'loss': 0.0013, 'grad_norm': 7.199844636451961, 'learning_rate': 9.395695364238411e-07, 'completion_length': 77.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.03228759765625, 'clip_ratio': 0.0, 'epoch': 0.48} 6%|▌ | 73/1208 [1:12:11<13:02:06, 41.35s/it]Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 174600: cache has only 0 modules Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 175473: cache has only 0 modules 6%|▌ | 74/1208 [1:12:50<12:46:27, 40.55s/it] {'loss': 0.0019, 'grad_norm': 10.758357645688788, 'learning_rate': 9.387417218543046e-07, 'completion_length': 85.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5175491571426392, 'kl': 0.0465087890625, 'clip_ratio': 0.0, 'epoch': 0.49} 6%|▌ | 74/1208 [1:12:50<12:46:27, 40.55s/it]Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 176346: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 177219: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 178092: cache has only 0 modules [Step 74] loss_orig = 0.003286, loss_refine = -0.124149 [Step 74] loss_orig = 0.000745, loss_refine = -1.133547 [Step 74] loss_orig = 0.001807, loss_refine = -1.133191 [Step 74] loss_orig = 0.001717, loss_refine = -0.124163[Step 74] loss_orig = 0.000637, loss_refine = -0.124736 [Step 74] loss_orig = 0.000906, loss_refine = -0.124832[Step 74] loss_orig = 0.001256, loss_refine = 0.883604 [Step 74] loss_orig = 0.001324, loss_refine = 1.892084 6%|▌ | 75/1208 [1:13:54<14:58:43, 47.59s/it] {'loss': 0.0012, 'grad_norm': 22.040209318398023, 'learning_rate': 9.379139072847681e-07, 'completion_length': 102.20833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.2083333333333335, 'reward_std': 0.48464709520339966, 'kl': 0.03070068359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.5} 6%|▌ | 75/1208 [1:13:54<14:58:43, 47.59s/it]Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 178965: cache has only 0 modules Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 179838: cache has only 0 modules 6%|▋ | 76/1208 [1:14:29<13:44:05, 43.68s/it] {'loss': 0.0016, 'grad_norm': 7.051502460904095, 'learning_rate': 9.370860927152318e-07, 'completion_length': 85.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.04107666015625, 'clip_ratio': 0.0, 'epoch': 0.5} 6%|▋ | 76/1208 [1:14:29<13:44:05, 43.68s/it]Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 180711: cache has only 0 modules Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 181584: cache has only 0 modules 6%|▋ | 77/1208 [1:15:20<14:27:55, 46.04s/it] {'loss': 0.0014, 'grad_norm': 14.024488705492196, 'learning_rate': 9.362582781456954e-07, 'completion_length': 109.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.034912109375, 'clip_ratio': 0.0, 'epoch': 0.51} 6%|▋ | 77/1208 [1:15:20<14:27:55, 46.04s/it]Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 182457: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 183330: cache has only 0 modules [Step 77] loss_orig = -0.352206, loss_refine = 0.001041[Step 77] loss_orig = -0.352834, loss_refine = 0.000911 [Step 77] loss_orig = -0.351651, loss_refine = 0.001263[Step 77] loss_orig = -0.343822, loss_refine = 0.000759[Step 77] loss_orig = -0.353163, loss_refine = 0.002200 [Step 77] loss_orig = -0.352083, loss_refine = 0.000856 [Step 77] loss_orig = 2.475647, loss_refine = 0.001325 [Step 77] loss_orig = -0.352327, loss_refine = 0.000973 Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 184203: cache has only 0 modules 6%|▋ | 78/1208 [1:16:08<14:35:49, 46.50s/it] {'loss': 0.0011, 'grad_norm': 4.607504338770688, 'learning_rate': 9.354304635761588e-07, 'completion_length': 85.5, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.2960252861181895, 'kl': 0.04144287109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.52} 6%|▋ | 78/1208 [1:16:08<14:35:49, 46.50s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 185076: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 185949: cache has only 0 modules 7%|▋ | 79/1208 [1:16:56<14:44:40, 47.02s/it] {'loss': 0.0012, 'grad_norm': 6.138953466102459, 'learning_rate': 9.346026490066224e-07, 'completion_length': 105.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0311279296875, 'clip_ratio': 0.0, 'epoch': 0.52} 7%|▋ | 79/1208 [1:16:56<14:44:40, 47.02s/it]Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 186822: cache has only 0 modules Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 187695: cache has only 0 modules 7%|▋ | 80/1208 [1:17:36<14:02:38, 44.82s/it] {'loss': 0.001, 'grad_norm': 3.4364125809774104, 'learning_rate': 9.337748344370861e-07, 'completion_length': 95.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.02392578125, 'clip_ratio': 0.0, 'epoch': 0.53} 7%|▋ | 80/1208 [1:17:36<14:02:38, 44.82s/it]Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 188568: cache has only 0 modules Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 189441: cache has only 0 modules 7%|▋ | 81/1208 [1:18:14<13:24:28, 42.83s/it] {'loss': 0.0007, 'grad_norm': 7.8243257262582855, 'learning_rate': 9.329470198675497e-07, 'completion_length': 86.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.5260358154773712, 'kl': 0.0177001953125, 'clip_ratio': 0.0, 'epoch': 0.54} 7%|▋ | 81/1208 [1:18:14<13:24:28, 42.83s/it]Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 190314: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 191187: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 192060: cache has only 0 modules [Step 81] loss_orig = -0.351709, loss_refine = -0.724045[Step 81] loss_orig = 2.477127, loss_refine = -0.723663 [Step 81] loss_orig = -0.352459, loss_refine = -0.722713[Step 81] loss_orig = -0.351182, loss_refine = 1.207895 [Step 81] loss_orig = -0.351663, loss_refine = 1.208691 [Step 81] loss_orig = -0.352076, loss_refine = 1.207899 [Step 81] loss_orig = -0.352747, loss_refine = -0.723333 [Step 81] loss_orig = -0.352787, loss_refine = -0.723479 7%|▋ | 82/1208 [1:19:09<14:35:08, 46.63s/it] {'loss': 0.001, 'grad_norm': 6.517908964531105, 'learning_rate': 9.321192052980132e-07, 'completion_length': 92.29166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.4082186420758565, 'kl': 0.03424072265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.54} 7%|▋ | 82/1208 [1:19:09<14:35:08, 46.63s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 192933: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 193806: cache has only 0 modules 7%|▋ | 83/1208 [1:19:46<13:36:31, 43.55s/it] {'loss': 0.0013, 'grad_norm': 6.345283591572631, 'learning_rate': 9.312913907284767e-07, 'completion_length': 98.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.031982421875, 'clip_ratio': 0.0, 'epoch': 0.55} 7%|▋ | 83/1208 [1:19:46<13:36:31, 43.55s/it]Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 194679: cache has only 0 modules Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 195552: cache has only 0 modules 7%|▋ | 84/1208 [1:20:29<13:35:24, 43.53s/it] {'loss': 0.0007, 'grad_norm': 6.658451367601168, 'learning_rate': 9.304635761589404e-07, 'completion_length': 94.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.016265869140625, 'clip_ratio': 0.0, 'epoch': 0.56} 7%|▋ | 84/1208 [1:20:29<13:35:24, 43.53s/it]Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 196425: cache has only 0 modules Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 197298: cache has only 0 modules 7%|▋ | 85/1208 [1:21:12<13:32:01, 43.38s/it] {'loss': 0.0011, 'grad_norm': 54.76608255985422, 'learning_rate': 9.296357615894039e-07, 'completion_length': 105.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.44403792917728424, 'kl': 0.02630615234375, 'clip_ratio': 0.0, 'epoch': 0.56} 7%|▋ | 85/1208 [1:21:12<13:32:01, 43.38s/it]Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 198171: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 199044: cache has only 0 modules 7%|▋ | 86/1208 [1:21:55<13:25:27, 43.07s/it] {'loss': 0.0011, 'grad_norm': 19.787114419775634, 'learning_rate': 9.288079470198675e-07, 'completion_length': 104.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.02801513671875, 'clip_ratio': 0.0, 'epoch': 0.57} 7%|▋ | 86/1208 [1:21:55<13:25:27, 43.07s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 199917: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'scan qr code'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 200790: cache has only 0 modules [Step 86] loss_orig = 0.000994, loss_refine = 0.001661[Step 86] loss_orig = 0.000730, loss_refine = 0.001912[Step 86] loss_orig = 0.001450, loss_refine = 0.001286[Step 86] loss_orig = 0.001217, loss_refine = 0.001498 [Step 86] loss_orig = 0.001023, loss_refine = 0.001624 [Step 86] loss_orig = 0.002391, loss_refine = 0.001341 [Step 86] loss_orig = 0.001352, loss_refine = 0.000808 [Step 86] loss_orig = 0.001778, loss_refine = 0.002033 Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 201663: cache has only 0 modules 7%|▋ | 87/1208 [1:22:50<14:34:40, 46.82s/it] {'loss': 0.0013, 'grad_norm': 4.338736929312301, 'learning_rate': 9.279801324503311e-07, 'completion_length': 104.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.0301513671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.58} 7%|▋ | 87/1208 [1:22:50<14:34:40, 46.82s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 202536: cache has only 0 modules Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 203409: cache has only 0 modules 7%|▋ | 88/1208 [1:23:30<13:54:57, 44.73s/it] {'loss': 0.0018, 'grad_norm': 10.226465878727364, 'learning_rate': 9.271523178807946e-07, 'completion_length': 103.3125, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.0625, 'reward_std': 0.5876962244510651, 'kl': 0.0447998046875, 'clip_ratio': 0.0, 'epoch': 0.58} 7%|▋ | 88/1208 [1:23:30<13:54:57, 44.73s/it]Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 204282: cache has only 0 modules Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 205155: cache has only 0 modules 7%|▋ | 89/1208 [1:24:06<13:06:25, 42.17s/it] {'loss': 0.0012, 'grad_norm': 20.723848162717093, 'learning_rate': 9.263245033112582e-07, 'completion_length': 91.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0302734375, 'clip_ratio': 0.0, 'epoch': 0.59} 7%|▋ | 89/1208 [1:24:06<13:06:25, 42.17s/it]Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 206028: cache has only 0 modules Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 206901: cache has only 0 modules 7%|▋ | 90/1208 [1:24:40<12:17:01, 39.55s/it] {'loss': 0.0012, 'grad_norm': 15.351225684945849, 'learning_rate': 9.254966887417218e-07, 'completion_length': 84.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4355512708425522, 'kl': 0.03125, 'clip_ratio': 0.0, 'epoch': 0.6} 7%|▋ | 90/1208 [1:24:40<12:17:01, 39.55s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 207774: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 208647: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [858, 108]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 209520: cache has only 0 modules [Step 90] loss_orig = 0.001742, loss_refine = 0.195973[Step 90] loss_orig = 0.001414, loss_refine = 0.195726[Step 90] loss_orig = 0.003353, loss_refine = 0.196039 [Step 90] loss_orig = 0.001351, loss_refine = 0.196155[Step 90] loss_orig = 0.001514, loss_refine = -1.362768 [Step 90] loss_orig = 0.001295, loss_refine = -1.363519 [Step 90] loss_orig = 0.003161, loss_refine = 1.761633 [Step 90] loss_orig = 0.001988, loss_refine = 0.195931 8%|▊ | 91/1208 [1:25:29<13:10:22, 42.46s/it] {'loss': 0.0016, 'grad_norm': 6.670605907353391, 'learning_rate': 9.246688741721855e-07, 'completion_length': 95.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.21362332503000894, 'kl': 0.0404052734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.6} 8%|▊ | 91/1208 [1:25:29<13:10:22, 42.46s/it]Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 210393: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 211266: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge'. Your previous answer was [{'action': 'click', 'coordinate': [1278, 1556]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 212139: cache has only 0 modules [Step 91] loss_orig = 0.000793, loss_refine = 1.208969[Step 91] loss_orig = 0.000886, loss_refine = -0.722792 [Step 91] loss_orig = 0.000808, loss_refine = -0.722279 [Step 91] loss_orig = 0.001822, loss_refine = -0.723776 [Step 91] loss_orig = 0.001190, loss_refine = 1.208323 [Step 91] loss_orig = 0.001092, loss_refine = 1.208846[Step 91] loss_orig = 0.003305, loss_refine = -0.723879 [Step 91] loss_orig = 0.002004, loss_refine = -0.723118 8%|▊ | 92/1208 [1:26:23<14:16:30, 46.05s/it] {'loss': 0.0013, 'grad_norm': 12.21096456800453, 'learning_rate': 9.23841059602649e-07, 'completion_length': 88.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.035888671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 0.61} 8%|▊ | 92/1208 [1:26:23<14:16:30, 46.05s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 213012: cache has only 0 modules Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 213885: cache has only 0 modules 8%|▊ | 93/1208 [1:27:04<13:43:49, 44.33s/it] {'loss': 0.0106, 'grad_norm': 14.47095325623087, 'learning_rate': 9.230132450331125e-07, 'completion_length': 88.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.49871626496315, 'kl': 0.26611328125, 'clip_ratio': 0.0, 'epoch': 0.62} 8%|▊ | 93/1208 [1:27:04<13:43:49, 44.33s/it]Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 214758: cache has only 0 modules Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 215631: cache has only 0 modules 8%|▊ | 94/1208 [1:27:42<13:11:27, 42.63s/it] {'loss': 0.0015, 'grad_norm': 20.050779900728656, 'learning_rate': 9.221854304635761e-07, 'completion_length': 85.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03729248046875, 'clip_ratio': 0.0, 'epoch': 0.62} 8%|▊ | 94/1208 [1:27:42<13:11:27, 42.63s/it]Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 216504: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 217377: cache has only 0 modules 8%|▊ | 95/1208 [1:28:21<12:51:37, 41.60s/it] {'loss': 0.0014, 'grad_norm': 13.309052342232564, 'learning_rate': 9.213576158940397e-07, 'completion_length': 93.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.035888671875, 'clip_ratio': 0.0, 'epoch': 0.63} 8%|▊ | 95/1208 [1:28:21<12:51:37, 41.60s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 218250: cache has only 0 modules Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 219123: cache has only 0 modules 8%|▊ | 96/1208 [1:29:02<12:46:51, 41.38s/it] {'loss': 0.0016, 'grad_norm': 13.58318608101913, 'learning_rate': 9.205298013245033e-07, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.49871626496315, 'kl': 0.039306640625, 'clip_ratio': 0.0, 'epoch': 0.64} 8%|▊ | 96/1208 [1:29:02<12:46:51, 41.38s/it]Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 219996: cache has only 0 modules Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 220869: cache has only 0 modules 8%|▊ | 97/1208 [1:29:44<12:48:00, 41.48s/it] {'loss': 0.0016, 'grad_norm': 17.942857009368005, 'learning_rate': 9.197019867549668e-07, 'completion_length': 90.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.3720118999481201, 'kl': 0.0400390625, 'clip_ratio': 0.0, 'epoch': 0.64} 8%|▊ | 97/1208 [1:29:44<12:48:00, 41.48s/it]Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 221742: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 222615: cache has only 0 modules [Step 97] loss_orig = -0.538164, loss_refine = -0.723050[Step 97] loss_orig = -0.538221, loss_refine = 1.208635 [Step 97] loss_orig = -0.539114, loss_refine = 1.208941 [Step 97] loss_orig = -0.538471, loss_refine = 1.208422[Step 97] loss_orig = 1.623637, loss_refine = -0.723634 [Step 97] loss_orig = -0.538643, loss_refine = -0.723489[Step 97] loss_orig = -0.537536, loss_refine = -0.722964 [Step 97] loss_orig = 1.625184, loss_refine = -0.723038 Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 223488: cache has only 0 modules 8%|▊ | 98/1208 [1:30:32<13:25:44, 43.55s/it] {'loss': 0.0013, 'grad_norm': 8.598201647049935, 'learning_rate': 9.188741721854304e-07, 'completion_length': 90.83333333333333, 'rewards/accuracy_reward_action': 0.8333333333333334, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.48112308979034424, 'kl': 0.0465087890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 0.65} 8%|▊ | 98/1208 [1:30:32<13:25:44, 43.55s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 224361: cache has only 0 modules Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 225234: cache has only 0 modules 8%|▊ | 99/1208 [1:31:07<12:35:48, 40.89s/it] {'loss': 0.0031, 'grad_norm': 5.2829231065661, 'learning_rate': 9.18046357615894e-07, 'completion_length': 85.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0771484375, 'clip_ratio': 0.0, 'epoch': 0.66} 8%|▊ | 99/1208 [1:31:07<12:35:48, 40.89s/it]Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 226107: cache has only 0 modules Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 226980: cache has only 0 modules 8%|▊ | 100/1208 [1:31:48<12:36:02, 40.94s/it] {'loss': 0.0015, 'grad_norm': 8.438713236823336, 'learning_rate': 9.172185430463576e-07, 'completion_length': 96.625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.579209566116333, 'kl': 0.03839111328125, 'clip_ratio': 0.0, 'epoch': 0.66} 8%|▊ | 100/1208 [1:31:48<12:36:02, 40.94s/it]Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 227853: cache has only 0 modules Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 228726: cache has only 0 modules 8%|▊ | 101/1208 [1:32:22<11:59:02, 38.97s/it] {'loss': 0.0009, 'grad_norm': 9.787409244999614, 'learning_rate': 9.163907284768212e-07, 'completion_length': 84.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.02215576171875, 'clip_ratio': 0.0, 'epoch': 0.67} 8%|▊ | 101/1208 [1:32:22<11:59:02, 38.97s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 229599: cache has only 0 modules Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 230472: cache has only 0 modules 8%|▊ | 102/1208 [1:33:01<11:56:23, 38.86s/it] {'loss': 0.0013, 'grad_norm': 84.67862242182227, 'learning_rate': 9.155629139072847e-07, 'completion_length': 86.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.03179931640625, 'clip_ratio': 0.0, 'epoch': 0.68} 8%|▊ | 102/1208 [1:33:01<11:56:23, 38.86s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 231345: cache has only 0 modules Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 232218: cache has only 0 modules 9%|▊ | 103/1208 [1:33:40<11:55:52, 38.87s/it] {'loss': 0.0014, 'grad_norm': 8.72666002749579, 'learning_rate': 9.147350993377483e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.8711025416851044, 'kl': 0.034912109375, 'clip_ratio': 0.0, 'epoch': 0.68} 9%|▊ | 103/1208 [1:33:40<11:55:52, 38.87s/it]Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 233091: cache has only 0 modules Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 233964: cache has only 0 modules 9%|▊ | 104/1208 [1:34:23<12:16:48, 40.04s/it] {'loss': 0.0019, 'grad_norm': 6.8768978804087775, 'learning_rate': 9.139072847682119e-07, 'completion_length': 88.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4355512708425522, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'epoch': 0.69} 9%|▊ | 104/1208 [1:34:23<12:16:48, 40.04s/it]Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 234837: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Tray Input Indicator - Chinese (Simplified, China)'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 235710: cache has only 0 modules [Step 104] loss_orig = -0.351978, loss_refine = 0.682780[Step 104] loss_orig = -0.351133, loss_refine = 1.775151[Step 104] loss_orig = -0.351705, loss_refine = -0.407328 [Step 104] loss_orig = -0.351888, loss_refine = -0.407135 [Step 104] loss_orig = 2.475763, loss_refine = -0.403287 [Step 104] loss_orig = -0.351558, loss_refine = -1.499472[Step 104] loss_orig = -0.352308, loss_refine = 0.683973 [Step 104] loss_orig = -0.352050, loss_refine = -0.407342 Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 236583: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 237456: cache has only 0 modules [Step 104] loss_orig = 0.000730, loss_refine = 0.001012 [Step 104] loss_orig = 0.000480, loss_refine = 0.000937[Step 104] loss_orig = 0.001902, loss_refine = 0.000914[Step 104] loss_orig = 0.000903, loss_refine = 0.000525 [Step 104] loss_orig = 0.000524, loss_refine = 0.000953[Step 104] loss_orig = 0.000501, loss_refine = 0.000638 [Step 104] loss_orig = 0.001287, loss_refine = 0.000287 [Step 104] loss_orig = 0.001756, loss_refine = 0.001586 9%|▊ | 105/1208 [1:35:47<16:20:47, 53.35s/it] {'loss': 0.0015, 'grad_norm': 8.770185672347475, 'learning_rate': 9.130794701986754e-07, 'completion_length': 106.59375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.96875, 'reward': 2.09375, 'reward_std': 0.4058080464601517, 'kl': 0.03314208984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.3125, 'epoch': 0.7} 9%|▊ | 105/1208 [1:35:47<16:20:47, 53.35s/it]Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 238329: cache has only 0 modules Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 239202: cache has only 0 modules 9%|▉ | 106/1208 [1:36:31<15:30:01, 50.64s/it] {'loss': 0.0011, 'grad_norm': 4.966808563050535, 'learning_rate': 9.122516556291391e-07, 'completion_length': 101.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.02685546875, 'clip_ratio': 0.0, 'epoch': 0.7} 9%|▉ | 106/1208 [1:36:31<15:30:01, 50.64s/it]Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 240075: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 240948: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 241821: cache has only 0 modules [Step 106] loss_orig = 0.002863, loss_refine = 0.542691[Step 106] loss_orig = 0.001694, loss_refine = 0.542730 [Step 106] loss_orig = 0.001561, loss_refine = 0.541743 [Step 106] loss_orig = 0.003615, loss_refine = 0.541359 [Step 106] loss_orig = 0.001968, loss_refine = -1.618555 [Step 106] loss_orig = 0.002410, loss_refine = 0.544736 [Step 106] loss_orig = 0.002419, loss_refine = 0.540997[Step 106] loss_orig = 0.002093, loss_refine = -1.618255 9%|▉ | 107/1208 [1:37:39<17:01:18, 55.66s/it] {'loss': 0.0018, 'grad_norm': 3.9959284963872945, 'learning_rate': 9.114238410596026e-07, 'completion_length': 94.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.045654296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.71} 9%|▉ | 107/1208 [1:37:39<17:01:18, 55.66s/it]Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 242694: cache has only 0 modules Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 243567: cache has only 0 modules 9%|▉ | 108/1208 [1:38:21<15:46:59, 51.65s/it] {'loss': 0.0011, 'grad_norm': 7.081331467204198, 'learning_rate': 9.105960264900662e-07, 'completion_length': 95.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0286865234375, 'clip_ratio': 0.0, 'epoch': 0.72} 9%|▉ | 108/1208 [1:38:21<15:46:59, 51.65s/it]Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 244440: cache has only 0 modules Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 245313: cache has only 0 modules 9%|▉ | 109/1208 [1:38:53<13:57:03, 45.70s/it] {'loss': 0.0023, 'grad_norm': 1.2230772786961959, 'learning_rate': 9.097682119205297e-07, 'completion_length': 75.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0577392578125, 'clip_ratio': 0.0, 'epoch': 0.72} 9%|▉ | 109/1208 [1:38:53<13:57:03, 45.70s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 246186: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 247059: cache has only 0 modules 9%|▉ | 110/1208 [1:39:38<13:51:54, 45.46s/it] {'loss': 0.0019, 'grad_norm': 22.77680983042136, 'learning_rate': 9.089403973509934e-07, 'completion_length': 89.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.5625, 'reward_std': 0.4955156147480011, 'kl': 0.04638671875, 'clip_ratio': 0.0, 'epoch': 0.73} 9%|▉ | 110/1208 [1:39:38<13:51:54, 45.46s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 247932: cache has only 0 modules Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 248805: cache has only 0 modules 9%|▉ | 111/1208 [1:40:14<13:00:41, 42.70s/it] {'loss': 0.0009, 'grad_norm': 8.636261877818841, 'learning_rate': 9.081125827814569e-07, 'completion_length': 76.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.02154541015625, 'clip_ratio': 0.0, 'epoch': 0.74} 9%|▉ | 111/1208 [1:40:14<13:00:41, 42.70s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 249678: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'flod this content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 250551: cache has only 0 modules [Step 111] loss_orig = 0.001717, loss_refine = 0.734419[Step 111] loss_orig = 0.000874, loss_refine = -1.204616[Step 111] loss_orig = 0.001407, loss_refine = 0.725082 [Step 111] loss_orig = 0.001145, loss_refine = 0.725096[Step 111] loss_orig = 0.000945, loss_refine = -1.206422 [Step 111] loss_orig = 0.000481, loss_refine = -1.204470 [Step 111] loss_orig = 0.000670, loss_refine = 0.725187 [Step 111] loss_orig = 0.001927, loss_refine = 0.724876 Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 251424: cache has only 0 modules 9%|▉ | 112/1208 [1:41:03<13:34:11, 44.57s/it] {'loss': 0.0018, 'grad_norm': 17.006154560950517, 'learning_rate': 9.072847682119204e-07, 'completion_length': 91.04166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.1666666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.0284423828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 0.74} 9%|▉ | 112/1208 [1:41:03<13:34:11, 44.57s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 252297: cache has only 0 modules Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 253170: cache has only 0 modules 9%|▉ | 113/1208 [1:41:46<13:27:00, 44.22s/it] {'loss': 0.0013, 'grad_norm': 14.834682014798611, 'learning_rate': 9.06456953642384e-07, 'completion_length': 90.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0321044921875, 'clip_ratio': 0.0, 'epoch': 0.75} 9%|▉ | 113/1208 [1:41:46<13:27:00, 44.22s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 254043: cache has only 0 modules Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 254916: cache has only 0 modules 9%|▉ | 114/1208 [1:42:22<12:37:54, 41.57s/it] {'loss': 0.0011, 'grad_norm': 11.649084328774011, 'learning_rate': 9.056291390728477e-07, 'completion_length': 74.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.6034669280052185, 'kl': 0.02862548828125, 'clip_ratio': 0.0, 'epoch': 0.75} 9%|▉ | 114/1208 [1:42:22<12:37:54, 41.57s/it]Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 255789: cache has only 0 modules Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 256662: cache has only 0 modules 10%|▉ | 115/1208 [1:43:01<12:23:07, 40.79s/it] {'loss': 0.0016, 'grad_norm': 5.440505713047385, 'learning_rate': 9.048013245033113e-07, 'completion_length': 86.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.03955078125, 'clip_ratio': 0.0, 'epoch': 0.76} 10%|▉ | 115/1208 [1:43:01<12:23:07, 40.79s/it]Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 257535: cache has only 0 modules Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 258408: cache has only 0 modules 10%|▉ | 116/1208 [1:43:41<12:21:04, 40.72s/it] {'loss': 0.0018, 'grad_norm': 18.680561793587575, 'learning_rate': 9.039735099337747e-07, 'completion_length': 95.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4629100561141968, 'kl': 0.04541015625, 'clip_ratio': 0.0, 'epoch': 0.77} 10%|▉ | 116/1208 [1:43:41<12:21:04, 40.72s/it]Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 259281: cache has only 0 modules Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 260154: cache has only 0 modules 10%|▉ | 117/1208 [1:44:16<11:47:36, 38.92s/it] {'loss': 0.0009, 'grad_norm': 4.667596023410489, 'learning_rate': 9.031456953642384e-07, 'completion_length': 76.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0224609375, 'clip_ratio': 0.0, 'epoch': 0.77} 10%|▉ | 117/1208 [1:44:16<11:47:36, 38.92s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 261027: cache has only 0 modules Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 261900: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Comments'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 262773: cache has only 0 modules [Step 117] loss_orig = 0.000811, loss_refine = -0.660601[Step 117] loss_orig = 0.001132, loss_refine = 0.662415 [Step 117] loss_orig = 0.000798, loss_refine = 1.984835[Step 117] loss_orig = 0.001530, loss_refine = 0.662350 [Step 117] loss_orig = 0.000758, loss_refine = -0.660938 [Step 117] loss_orig = 0.001461, loss_refine = -0.660269[Step 117] loss_orig = 0.001202, loss_refine = -0.660601 [Step 117] loss_orig = 0.001356, loss_refine = -0.660451 10%|▉ | 118/1208 [1:45:16<13:40:36, 45.17s/it] {'loss': 0.0011, 'grad_norm': 17.36733548913131, 'learning_rate': 9.02317880794702e-07, 'completion_length': 91.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.40627966324488324, 'kl': 0.0308837890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 0.78} 10%|▉ | 118/1208 [1:45:16<13:40:36, 45.17s/it]Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 263646: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 264519: cache has only 0 modules [Step 118] loss_orig = 0.001502, loss_refine = 0.000384 [Step 118] loss_orig = 0.001321, loss_refine = 0.000415[Step 118] loss_orig = 0.001159, loss_refine = 0.000698 [Step 118] loss_orig = 0.001143, loss_refine = 0.001546 [Step 118] loss_orig = 0.000747, loss_refine = 0.002610[Step 118] loss_orig = 0.000835, loss_refine = 0.000311[Step 118] loss_orig = 0.000984, loss_refine = 0.000492 [Step 118] loss_orig = 0.000961, loss_refine = 0.001881 Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 265392: cache has only 0 modules 10%|▉ | 119/1208 [1:46:08<14:17:57, 47.27s/it] {'loss': 0.0013, 'grad_norm': 11.316811277049682, 'learning_rate': 9.014900662251655e-07, 'completion_length': 78.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.03173828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.79} 10%|▉ | 119/1208 [1:46:08<14:17:57, 47.27s/it]Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 266265: cache has only 0 modules Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 267138: cache has only 0 modules 10%|▉ | 120/1208 [1:46:46<13:29:00, 44.61s/it] {'loss': 0.0011, 'grad_norm': 9.643814330895454, 'learning_rate': 9.006622516556291e-07, 'completion_length': 87.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.02838134765625, 'clip_ratio': 0.0, 'epoch': 0.79} 10%|▉ | 120/1208 [1:46:46<13:29:00, 44.61s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 268011: cache has only 0 modules Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 268884: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1444, 528]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 269757: cache has only 0 modules [Step 120] loss_orig = 0.001078, loss_refine = 0.355718 [Step 120] loss_orig = 0.000848, loss_refine = 0.353971[Step 120] loss_orig = 0.000920, loss_refine = 0.354349 [Step 120] loss_orig = 0.003880, loss_refine = 0.354555 [Step 120] loss_orig = 0.001275, loss_refine = -2.473588 [Step 120] loss_orig = 0.002348, loss_refine = 0.354487[Step 120] loss_orig = 0.001197, loss_refine = 0.354617 [Step 120] loss_orig = 0.000822, loss_refine = 0.354197 10%|█ | 121/1208 [1:47:49<15:06:47, 50.05s/it] {'loss': 0.0017, 'grad_norm': 19.786021434479068, 'learning_rate': 8.998344370860927e-07, 'completion_length': 107.75, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.0416666666666665, 'reward_std': 0.2960252861181895, 'kl': 0.049560546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 0.8} 10%|█ | 121/1208 [1:47:49<15:06:47, 50.05s/it]Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 270630: cache has only 0 modules Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 271503: cache has only 0 modules 10%|█ | 122/1208 [1:48:27<14:01:10, 46.47s/it] {'loss': 0.002, 'grad_norm': 10.827165024133212, 'learning_rate': 8.990066225165562e-07, 'completion_length': 79.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0491943359375, 'clip_ratio': 0.0, 'epoch': 0.81} 10%|█ | 122/1208 [1:48:27<14:01:10, 46.47s/it]Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 272376: cache has only 0 modules Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 273249: cache has only 0 modules 10%|█ | 123/1208 [1:49:02<12:57:19, 42.99s/it] {'loss': 0.0009, 'grad_norm': 10.35029720335319, 'learning_rate': 8.981788079470198e-07, 'completion_length': 85.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5175491571426392, 'kl': 0.02227783203125, 'clip_ratio': 0.0, 'epoch': 0.81} 10%|█ | 123/1208 [1:49:02<12:57:19, 42.99s/it]Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 274122: cache has only 0 modules Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 274995: cache has only 0 modules 10%|█ | 124/1208 [1:49:41<12:33:07, 41.69s/it] {'loss': 0.0016, 'grad_norm': 12.862465490495397, 'learning_rate': 8.973509933774834e-07, 'completion_length': 89.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4355512708425522, 'kl': 0.03955078125, 'clip_ratio': 0.0, 'epoch': 0.82} 10%|█ | 124/1208 [1:49:41<12:33:07, 41.69s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 275868: cache has only 0 modules Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 276741: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 277614: cache has only 0 modules [Step 124] loss_orig = 0.000797, loss_refine = -0.351952[Step 124] loss_orig = 0.001345, loss_refine = -0.352770[Step 124] loss_orig = 0.002963, loss_refine = 2.476832[Step 124] loss_orig = 0.001395, loss_refine = -0.352523 [Step 124] loss_orig = 0.000353, loss_refine = -0.352636 [Step 124] loss_orig = 0.001503, loss_refine = -0.352002 [Step 124] loss_orig = 0.003976, loss_refine = -0.352210 [Step 124] loss_orig = 0.000861, loss_refine = -0.351302 10%|█ | 125/1208 [1:50:32<13:24:45, 44.58s/it] {'loss': 0.0013, 'grad_norm': 11.75458227291396, 'learning_rate': 8.965231788079471e-07, 'completion_length': 86.95833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3535533845424652, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 0.83} 10%|█ | 125/1208 [1:50:32<13:24:45, 44.58s/it]Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 278487: cache has only 0 modules Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 279360: cache has only 0 modules ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 280233: cache has only 0 modules [Step 125] loss_orig = 0.002298, loss_refine = 0.354876 [Step 125] loss_orig = 0.001717, loss_refine = -1.059151 [Step 125] loss_orig = 0.003118, loss_refine = -1.058782 [Step 125] loss_orig = 0.002433, loss_refine = 0.354189[Step 125] loss_orig = 0.003375, loss_refine = 0.354873[Step 125] loss_orig = 0.000636, loss_refine = 1.769866 [Step 125] loss_orig = 0.001226, loss_refine = -1.059672 [Step 125] loss_orig = 0.001012, loss_refine = 0.355087 10%|█ | 126/1208 [1:51:35<15:01:13, 49.98s/it] {'loss': 0.0013, 'grad_norm': 10.43681065929579, 'learning_rate': 8.956953642384105e-07, 'completion_length': 102.375, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.1666666666666665, 'reward_std': 0.39000560839970905, 'kl': 0.0396728515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.83} 10%|█ | 126/1208 [1:51:35<15:01:13, 49.98s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 281106: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 281979: cache has only 0 modules 11%|█ | 127/1208 [1:52:05<13:14:48, 44.12s/it] {'loss': 0.0014, 'grad_norm': 8.403946481530204, 'learning_rate': 8.948675496688741e-07, 'completion_length': 78.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.035400390625, 'clip_ratio': 0.0, 'epoch': 0.84} 11%|█ | 127/1208 [1:52:05<13:14:48, 44.12s/it]Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 282852: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 283725: cache has only 0 modules [Step 127] loss_orig = 0.001366, loss_refine = 0.002035[Step 127] loss_orig = 0.000739, loss_refine = 0.001775[Step 127] loss_orig = 0.001227, loss_refine = 0.001563 [Step 127] loss_orig = 0.001116, loss_refine = 0.001558 [Step 127] loss_orig = 0.000783, loss_refine = 0.000866[Step 127] loss_orig = 0.000653, loss_refine = 0.002252 [Step 127] loss_orig = 0.001263, loss_refine = 0.001903 [Step 127] loss_orig = 0.005077, loss_refine = 0.001983 Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 284598: cache has only 0 modules 11%|█ | 128/1208 [1:53:00<14:14:34, 47.48s/it] {'loss': 0.0017, 'grad_norm': 5.877128329982005, 'learning_rate': 8.940397350993378e-07, 'completion_length': 97.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.85} 11%|█ | 128/1208 [1:53:00<14:14:34, 47.48s/it]Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 285471: cache has only 0 modules Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 286344: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'choose watercolor brush style'. Your previous answer was [{'action': 'click', 'coordinate': [578, 2308]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 287217: cache has only 0 modules [Step 128] loss_orig = 0.000943, loss_refine = 0.354759[Step 128] loss_orig = 0.001288, loss_refine = -2.473932[Step 128] loss_orig = 0.000898, loss_refine = 0.354396 [Step 128] loss_orig = 0.000926, loss_refine = 0.354205 [Step 128] loss_orig = 0.001279, loss_refine = 0.353927 [Step 128] loss_orig = 0.001798, loss_refine = 0.354406 [Step 128] loss_orig = 0.000532, loss_refine = 0.354454 [Step 128] loss_orig = 0.000867, loss_refine = 0.354886 11%|█ | 129/1208 [1:53:54<14:47:09, 49.33s/it] {'loss': 0.0012, 'grad_norm': 3.748353800600055, 'learning_rate': 8.932119205298013e-07, 'completion_length': 91.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.03314208984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 0.85} 11%|█ | 129/1208 [1:53:54<14:47:09, 49.33s/it]Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 288090: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 288963: cache has only 0 modules [Step 129] loss_orig = 0.001459, loss_refine = -0.539185 [Step 129] loss_orig = 0.002479, loss_refine = -0.538060[Step 129] loss_orig = 0.000830, loss_refine = -0.538576 [Step 129] loss_orig = 0.001533, loss_refine = 1.620874 [Step 129] loss_orig = 0.001250, loss_refine = -0.539176 [Step 129] loss_orig = 0.001203, loss_refine = -0.535948 [Step 129] loss_orig = 0.001578, loss_refine = -0.538588 [Step 129] loss_orig = 0.001903, loss_refine = 1.623469 Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 289836: cache has only 0 modules 11%|█ | 130/1208 [1:54:47<15:08:01, 50.54s/it] {'loss': 0.0016, 'grad_norm': 23.88158871647325, 'learning_rate': 8.923841059602648e-07, 'completion_length': 90.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.037109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 0.86} 11%|█ | 130/1208 [1:54:47<15:08:01, 50.54s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 290709: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 291582: cache has only 0 modules [Step 130] loss_orig = 0.000859, loss_refine = 0.544517[Step 130] loss_orig = 0.001036, loss_refine = -1.618957[Step 130] loss_orig = 0.001763, loss_refine = 0.540620 [Step 130] loss_orig = 0.001060, loss_refine = 0.541065 [Step 130] loss_orig = 0.000725, loss_refine = -1.618895[Step 130] loss_orig = 0.000832, loss_refine = 0.541108 [Step 130] loss_orig = 0.002192, loss_refine = 0.541493 [Step 130] loss_orig = 0.001425, loss_refine = 0.541183 Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 292455: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'add alarm to the included controls'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 293328: cache has only 0 modules [Step 130] loss_orig = 0.000671, loss_refine = 1.623281 [Step 130] loss_orig = 0.002264, loss_refine = -0.539200[Step 130] loss_orig = 0.001186, loss_refine = -0.539714 [Step 130] loss_orig = 0.001407, loss_refine = -0.539481[Step 130] loss_orig = 0.000983, loss_refine = 0.540598 [Step 130] loss_orig = 0.001336, loss_refine = 0.541084 [Step 130] loss_orig = 0.001698, loss_refine = 0.541921 [Step 130] loss_orig = 0.000607, loss_refine = -1.619448 11%|█ | 131/1208 [1:56:09<17:53:54, 59.83s/it] {'loss': 0.0013, 'grad_norm': 7.149165809783546, 'learning_rate': 8.915562913907284e-07, 'completion_length': 96.5, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.96875, 'reward': 2.1875, 'reward_std': 0.3471825420856476, 'kl': 0.03131103515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 0.87} 11%|█ | 131/1208 [1:56:09<17:53:54, 59.83s/it]Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 294201: cache has only 0 modules Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 295074: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to song lyric'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 295947: cache has only 0 modules [Step 131] loss_orig = 0.001471, loss_refine = 0.506501[Step 131] loss_orig = 0.003395, loss_refine = 0.506163 [Step 131] loss_orig = 0.001871, loss_refine = -0.838678 [Step 131] loss_orig = 0.005906, loss_refine = 0.504879[Step 131] loss_orig = 0.001561, loss_refine = 0.504333 [Step 131] loss_orig = 0.002082, loss_refine = 0.505311 [Step 131] loss_orig = 0.001035, loss_refine = 0.504915 [Step 131] loss_orig = 0.001935, loss_refine = -2.182103 11%|█ | 132/1208 [1:57:00<17:08:04, 57.33s/it] {'loss': 0.0012, 'grad_norm': 15.376988169878317, 'learning_rate': 8.90728476821192e-07, 'completion_length': 88.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.24800793329874674, 'kl': 0.04241943359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 0.87} 11%|█ | 132/1208 [1:57:00<17:08:04, 57.33s/it]Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 296820: cache has only 0 modules Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 297693: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 298566: cache has only 0 modules [Step 132] loss_orig = 0.000878, loss_refine = -0.539472[Step 132] loss_orig = 0.000772, loss_refine = 1.621162 [Step 132] loss_orig = 0.000784, loss_refine = -0.539593[Step 132] loss_orig = 0.001884, loss_refine = -0.538752 [Step 132] loss_orig = 0.001417, loss_refine = -0.539054 [Step 132] loss_orig = 0.000654, loss_refine = -0.538605 [Step 132] loss_orig = 0.001221, loss_refine = 1.621735 [Step 132] loss_orig = 0.001001, loss_refine = -0.538911 11%|█ | 133/1208 [1:57:57<17:03:58, 57.15s/it] {'loss': 0.0023, 'grad_norm': 18.902060004379084, 'learning_rate': 8.899006622516556e-07, 'completion_length': 99.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.48112308979034424, 'kl': 0.057861328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 0.88} 11%|█ | 133/1208 [1:57:57<17:03:58, 57.15s/it]Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 299439: cache has only 0 modules Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 300312: cache has only 0 modules 11%|█ | 134/1208 [1:58:38<15:36:26, 52.31s/it] {'loss': 0.0027, 'grad_norm': 6.625418916813392, 'learning_rate': 8.890728476821192e-07, 'completion_length': 112.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.0682373046875, 'clip_ratio': 0.0, 'epoch': 0.89} 11%|█ | 134/1208 [1:58:38<15:36:26, 52.31s/it]Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 301185: cache has only 0 modules Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 302058: cache has only 0 modules 11%|█ | 135/1208 [1:59:09<13:40:21, 45.87s/it] {'loss': 0.0021, 'grad_norm': 13.419511777850293, 'learning_rate': 8.882450331125827e-07, 'completion_length': 78.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.05242919921875, 'clip_ratio': 0.0, 'epoch': 0.89} 11%|█ | 135/1208 [1:59:09<13:40:21, 45.87s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 302931: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 303804: cache has only 0 modules 11%|█▏ | 136/1208 [1:59:48<13:04:34, 43.91s/it] {'loss': 0.0012, 'grad_norm': 0.5093857190429054, 'learning_rate': 8.874172185430463e-07, 'completion_length': 94.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02960205078125, 'clip_ratio': 0.0, 'epoch': 0.9} 11%|█▏ | 136/1208 [1:59:48<13:04:34, 43.91s/it]Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 304677: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 305550: cache has only 0 modules 11%|█▏ | 137/1208 [2:00:35<13:16:59, 44.65s/it] {'loss': 0.0013, 'grad_norm': 4.424154853343471, 'learning_rate': 8.865894039735099e-07, 'completion_length': 100.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.03271484375, 'clip_ratio': 0.0, 'epoch': 0.91} 11%|█▏ | 137/1208 [2:00:35<13:16:59, 44.65s/it]Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 306423: cache has only 0 modules Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 307296: cache has only 0 modules 11%|█▏ | 138/1208 [2:01:11<12:30:50, 42.10s/it] {'loss': 0.0017, 'grad_norm': 3.829938279270203, 'learning_rate': 8.857615894039735e-07, 'completion_length': 93.125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.5345224738121033, 'kl': 0.0433349609375, 'clip_ratio': 0.0, 'epoch': 0.91} 11%|█▏ | 138/1208 [2:01:11<12:30:50, 42.10s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 308169: cache has only 0 modules Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 309042: cache has only 0 modules 12%|█▏ | 139/1208 [2:01:44<11:43:26, 39.48s/it] {'loss': 0.0018, 'grad_norm': 0.6938136505333059, 'learning_rate': 8.849337748344371e-07, 'completion_length': 74.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0450439453125, 'clip_ratio': 0.0, 'epoch': 0.92} 12%|█▏ | 139/1208 [2:01:44<11:43:26, 39.48s/it]Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 309915: cache has only 0 modules Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 310788: cache has only 0 modules 12%|█▏ | 140/1208 [2:02:22<11:35:34, 39.08s/it] {'loss': 0.0012, 'grad_norm': 12.48896509354997, 'learning_rate': 8.841059602649006e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.49022960662841797, 'kl': 0.0291748046875, 'clip_ratio': 0.0, 'epoch': 0.93} 12%|█▏ | 140/1208 [2:02:22<11:35:34, 39.08s/it]Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 311661: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 312534: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functions'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 313407: cache has only 0 modules [Step 140] loss_orig = 0.001250, loss_refine = 0.196676[Step 140] loss_orig = 0.001019, loss_refine = 1.756458[Step 140] loss_orig = 0.001158, loss_refine = -1.362768 [Step 140] loss_orig = 0.001280, loss_refine = -1.361777[Step 140] loss_orig = 0.001337, loss_refine = 0.196634 [Step 140] loss_orig = 0.002589, loss_refine = 0.197041[Step 140] loss_orig = 0.001429, loss_refine = 0.196097 [Step 140] loss_orig = 0.001194, loss_refine = 0.195803 12%|█▏ | 141/1208 [2:03:08<12:07:17, 40.90s/it] {'loss': 0.0039, 'grad_norm': 8.152455880629889, 'learning_rate': 8.832781456953642e-07, 'completion_length': 79.58333333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4583333333333335, 'reward_std': 0.5586560964584351, 'kl': 0.09423828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.93} 12%|█▏ | 141/1208 [2:03:08<12:07:17, 40.90s/it]Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 314280: cache has only 0 modules Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 315153: cache has only 0 modules 12%|█▏ | 142/1208 [2:03:59<13:02:58, 44.07s/it] {'loss': 0.0022, 'grad_norm': 6.839588008069575, 'learning_rate': 8.824503311258278e-07, 'completion_length': 122.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.0548095703125, 'clip_ratio': 0.0, 'epoch': 0.94} 12%|█▏ | 142/1208 [2:03:59<13:02:58, 44.07s/it]Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 316026: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'exchange target and source city'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 316899: cache has only 0 modules [Step 142] loss_orig = 0.000612, loss_refine = -0.539505[Step 142] loss_orig = 0.000991, loss_refine = -0.538023[Step 142] loss_orig = 0.000817, loss_refine = -0.538702 [Step 142] loss_orig = 0.003351, loss_refine = -0.539651[Step 142] loss_orig = 0.000709, loss_refine = 1.620661 [Step 142] loss_orig = 0.000772, loss_refine = -0.539486[Step 142] loss_orig = 0.000843, loss_refine = -0.539581 [Step 142] loss_orig = 0.001515, loss_refine = 1.620755 Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 317772: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 318645: cache has only 0 modules [Step 142] loss_orig = 0.001790, loss_refine = -0.539410[Step 142] loss_orig = 0.001119, loss_refine = -0.538898 [Step 142] loss_orig = 0.001814, loss_refine = -0.537072[Step 142] loss_orig = 0.001434, loss_refine = -0.539110[Step 142] loss_orig = 0.001194, loss_refine = 1.620399 [Step 142] loss_orig = 0.003793, loss_refine = 1.620482[Step 142] loss_orig = 0.001406, loss_refine = -0.536080 [Step 142] loss_orig = 0.002695, loss_refine = -0.539098 12%|█▏ | 143/1208 [2:05:17<16:02:37, 54.23s/it] {'loss': 0.0011, 'grad_norm': 19.03450415336884, 'learning_rate': 8.816225165562914e-07, 'completion_length': 105.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.2314550280570984, 'kl': 0.038818359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 0.95} 12%|█▏ | 143/1208 [2:05:17<16:02:37, 54.23s/it]Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 319518: cache has only 0 modules Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 320391: cache has only 0 modules 12%|█▏ | 144/1208 [2:05:48<14:00:10, 47.38s/it] {'loss': 0.0012, 'grad_norm': 0.30694718290957457, 'learning_rate': 8.807947019867549e-07, 'completion_length': 78.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03082275390625, 'clip_ratio': 0.0, 'epoch': 0.95} 12%|█▏ | 144/1208 [2:05:48<14:00:10, 47.38s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 321264: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 322137: cache has only 0 modules 12%|█▏ | 145/1208 [2:06:31<13:32:08, 45.84s/it] {'loss': 0.0021, 'grad_norm': 14.501284866669591, 'learning_rate': 8.799668874172185e-07, 'completion_length': 107.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.554741159081459, 'kl': 0.05322265625, 'clip_ratio': 0.0, 'epoch': 0.96} 12%|█▏ | 145/1208 [2:06:31<13:32:08, 45.84s/it]Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 323010: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 323883: cache has only 0 modules 12%|█▏ | 146/1208 [2:07:07<12:42:34, 43.08s/it] {'loss': 0.0015, 'grad_norm': 9.243072860765697, 'learning_rate': 8.79139072847682e-07, 'completion_length': 89.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 0.97} 12%|█▏ | 146/1208 [2:07:07<12:42:34, 43.08s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 324756: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'handwrite mode'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 325629: cache has only 0 modules [Step 146] loss_orig = 0.001289, loss_refine = 0.000822 [Step 146] loss_orig = 0.001091, loss_refine = 0.000976[Step 146] loss_orig = 0.000718, loss_refine = 0.001065 [Step 146] loss_orig = 0.000948, loss_refine = 0.000976 [Step 146] loss_orig = 0.000451, loss_refine = 0.000916 [Step 146] loss_orig = 0.000869, loss_refine = 0.000429 [Step 146] loss_orig = 0.001531, loss_refine = 0.000980 [Step 146] loss_orig = 0.000886, loss_refine = 0.000774 Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 326502: cache has only 0 modules 12%|█▏ | 147/1208 [2:07:57<13:14:39, 44.94s/it] {'loss': 0.0035, 'grad_norm': 3.8140137620062426, 'learning_rate': 8.783112582781457e-07, 'completion_length': 94.66666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.09027099609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 0.97} 12%|█▏ | 147/1208 [2:07:57<13:14:39, 44.94s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 327375: cache has only 0 modules Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 328248: cache has only 0 modules 12%|█▏ | 148/1208 [2:08:33<12:29:47, 42.44s/it] {'loss': 0.0014, 'grad_norm': 6.654409986850574, 'learning_rate': 8.774834437086093e-07, 'completion_length': 102.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.03424072265625, 'clip_ratio': 0.0, 'epoch': 0.98} 12%|█▏ | 148/1208 [2:08:33<12:29:47, 42.44s/it]Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 329121: cache has only 0 modules Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 329994: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 330867: cache has only 0 modules [Step 148] loss_orig = 0.001274, loss_refine = 0.725693[Step 148] loss_orig = 0.001741, loss_refine = -1.205730[Step 148] loss_orig = 0.002069, loss_refine = 0.726998 [Step 148] loss_orig = 0.004452, loss_refine = 0.726078 [Step 148] loss_orig = 0.000986, loss_refine = 0.725723[Step 148] loss_orig = 0.001569, loss_refine = -1.205085 [Step 148] loss_orig = 0.002163, loss_refine = 0.726668 [Step 148] loss_orig = 0.001714, loss_refine = -1.205177 12%|█▏ | 149/1208 [2:09:32<13:57:46, 47.47s/it] {'loss': 0.0016, 'grad_norm': 7.91712073708007, 'learning_rate': 8.766556291390727e-07, 'completion_length': 98.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.3450327714284261, 'kl': 0.04046630859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 0.99} 12%|█▏ | 149/1208 [2:09:32<13:57:46, 47.47s/it]Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 331740: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'add a new item'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 332613: cache has only 0 modules [Step 149] loss_orig = 0.000817, loss_refine = 0.540616[Step 149] loss_orig = 0.000555, loss_refine = 0.540907 [Step 149] loss_orig = 0.000598, loss_refine = 0.540916 [Step 149] loss_orig = 0.000804, loss_refine = 0.540585 [Step 149] loss_orig = 0.000979, loss_refine = 0.540606 [Step 149] loss_orig = 0.000652, loss_refine = -1.619464 [Step 149] loss_orig = 0.000375, loss_refine = -1.619127 [Step 149] loss_orig = 0.000731, loss_refine = 0.540537 Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 333486: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Copilot (Ctrl+Shift+.)'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 334359: cache has only 0 modules [Step 149] loss_orig = 0.002586, loss_refine = -2.473077 [Step 149] loss_orig = 0.002733, loss_refine = 0.354155 [Step 149] loss_orig = 0.001905, loss_refine = 0.354604[Step 149] loss_orig = 0.001570, loss_refine = 0.355726 [Step 149] loss_orig = 0.001362, loss_refine = 0.354924 [Step 149] loss_orig = 0.002620, loss_refine = 0.354722 [Step 149] loss_orig = 0.001274, loss_refine = 0.356191 [Step 149] loss_orig = 0.003166, loss_refine = 0.354760 12%|█▏ | 150/1208 [2:10:43<16:01:15, 54.51s/it] {'loss': 0.0011, 'grad_norm': 20.517896943349452, 'learning_rate': 8.758278145695363e-07, 'completion_length': 94.78125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 1.0, 'reward': 2.34375, 'reward_std': 0.2041158601641655, 'kl': 0.03546142578125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5625, 'epoch': 0.99} 12%|█▏ | 150/1208 [2:10:43<16:01:15, 54.51s/it]Start loss calc for inst: add a emoji /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.3333333432674408 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 335232: cache has only 0 modules Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.8333333730697632 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 336105: cache has only 0 modules 12%|█▎ | 151/1208 [2:11:37<15:56:19, 54.29s/it] {'loss': 0.0013, 'grad_norm': 8.45864008003913, 'learning_rate': 8.75e-07, 'completion_length': 90.00000381469727, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.583333358168602, 'rewards/format_reward': 1.0, 'reward': 2.5833334922790527, 'reward_std': 0.408231720328331, 'kl': 0.02716064453125, 'clip_ratio': 0.0, 'epoch': 1.0} 12%|█▎ | 151/1208 [2:11:37<15:56:19, 54.29s/it]Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 336978: cache has only 0 modules Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 337851: cache has only 0 modules 13%|█▎ | 152/1208 [2:12:18<14:43:37, 50.21s/it] {'loss': 0.0007, 'grad_norm': 5.9151201342616355, 'learning_rate': 8.741721854304636e-07, 'completion_length': 96.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.018585205078125, 'clip_ratio': 0.0, 'epoch': 1.01} 13%|█▎ | 152/1208 [2:12:18<14:43:37, 50.21s/it]Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 338724: cache has only 0 modules Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 339597: cache has only 0 modules 13%|█▎ | 153/1208 [2:12:56<13:41:36, 46.73s/it] {'loss': 0.0011, 'grad_norm': 0.2313358597801456, 'learning_rate': 8.733443708609271e-07, 'completion_length': 92.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0277099609375, 'clip_ratio': 0.0, 'epoch': 1.01} 13%|█▎ | 153/1208 [2:12:56<13:41:36, 46.73s/it]Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 340470: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'forwarding'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 341343: cache has only 0 modules [Step 153] loss_orig = 0.000776, loss_refine = 0.002139[Step 153] loss_orig = 0.001232, loss_refine = -1.077523[Step 153] loss_orig = 0.000981, loss_refine = 1.082648 [Step 153] loss_orig = 0.000993, loss_refine = -1.078123 [Step 153] loss_orig = 0.001770, loss_refine = 1.081123 [Step 153] loss_orig = 0.001652, loss_refine = -1.079206 [Step 153] loss_orig = 0.000339, loss_refine = 1.081025 [Step 153] loss_orig = 0.001576, loss_refine = 0.001749 Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 342216: cache has only 0 modules 13%|█▎ | 154/1208 [2:13:59<15:04:24, 51.48s/it] {'loss': 0.0025, 'grad_norm': 9.26918294006864, 'learning_rate': 8.725165562913907e-07, 'completion_length': 99.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.4629100561141968, 'kl': 0.0545654296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.02} 13%|█▎ | 154/1208 [2:13:59<15:04:24, 51.48s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 343089: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 343962: cache has only 0 modules [Step 154] loss_orig = 0.002504, loss_refine = -0.933996[Step 154] loss_orig = 0.000935, loss_refine = 0.938223 [Step 154] loss_orig = 0.000804, loss_refine = 0.937114[Step 154] loss_orig = 0.002287, loss_refine = 0.936648[Step 154] loss_orig = 0.000987, loss_refine = 0.936972 [Step 154] loss_orig = 0.000577, loss_refine = -0.933112 [Step 154] loss_orig = 0.001291, loss_refine = -0.933550 [Step 154] loss_orig = 0.000559, loss_refine = -0.934530 Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 344835: cache has only 0 modules 13%|█▎ | 155/1208 [2:14:59<15:46:34, 53.94s/it] {'loss': 0.0014, 'grad_norm': 15.890216322386564, 'learning_rate': 8.716887417218543e-07, 'completion_length': 103.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.3506905436515808, 'kl': 0.02813720703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.03} 13%|█▎ | 155/1208 [2:14:59<15:46:34, 53.94s/it]Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 345708: cache has only 0 modules Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 346581: cache has only 0 modules 13%|█▎ | 156/1208 [2:15:30<13:49:37, 47.32s/it] {'loss': 0.0016, 'grad_norm': 0.36836158705527106, 'learning_rate': 8.708609271523178e-07, 'completion_length': 92.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0400390625, 'clip_ratio': 0.0, 'epoch': 1.03} 13%|█▎ | 156/1208 [2:15:30<13:49:37, 47.32s/it]Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 347454: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element New Photo Album...'. Your previous answer was [{'action': 'click', 'coordinate': [338, 85]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 348327: cache has only 0 modules [Step 156] loss_orig = 0.001203, loss_refine = 0.840841 [Step 156] loss_orig = 0.001494, loss_refine = -0.503091 [Step 156] loss_orig = 0.001193, loss_refine = 0.840963 [Step 156] loss_orig = 0.001294, loss_refine = 0.842277 [Step 156] loss_orig = 0.001261, loss_refine = -0.503110 [Step 156] loss_orig = 0.002956, loss_refine = -1.846861 [Step 156] loss_orig = 0.005236, loss_refine = 0.840848 [Step 156] loss_orig = 0.001391, loss_refine = -0.502503 Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 349200: cache has only 0 modules 13%|█▎ | 157/1208 [2:16:21<14:07:47, 48.40s/it] {'loss': 0.0011, 'grad_norm': 11.48870695413679, 'learning_rate': 8.700331125827814e-07, 'completion_length': 83.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.4205243190129598, 'kl': 0.03704833984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.04} 13%|█▎ | 157/1208 [2:16:21<14:07:47, 48.40s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 350073: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 350946: cache has only 0 modules [Step 157] loss_orig = -0.538562, loss_refine = 1.851319[Step 157] loss_orig = -0.537594, loss_refine = -0.838365[Step 157] loss_orig = -0.538054, loss_refine = -0.838814 [Step 157] loss_orig = -0.537808, loss_refine = -0.838697 [Step 157] loss_orig = -0.538777, loss_refine = 0.504820 [Step 157] loss_orig = -0.538440, loss_refine = 0.505452[Step 157] loss_orig = 1.620971, loss_refine = -0.838804 [Step 157] loss_orig = 1.621144, loss_refine = 0.507868 Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 351819: cache has only 0 modules 13%|█▎ | 158/1208 [2:17:15<14:36:08, 50.06s/it] {'loss': 0.0013, 'grad_norm': 7.89023841976568, 'learning_rate': 8.692052980132451e-07, 'completion_length': 101.08333333333333, 'rewards/accuracy_reward_action': 0.8333333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4023112853368123, 'kl': 0.02984619140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.05} 13%|█▎ | 158/1208 [2:17:15<14:36:08, 50.06s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 352692: cache has only 0 modules Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 353565: cache has only 0 modules 13%|█▎ | 159/1208 [2:17:49<13:11:04, 45.25s/it] {'loss': 0.0011, 'grad_norm': 8.212171644927036, 'learning_rate': 8.683774834437085e-07, 'completion_length': 84.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.02862548828125, 'clip_ratio': 0.0, 'epoch': 1.05} 13%|█▎ | 159/1208 [2:17:49<13:11:04, 45.25s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 354438: cache has only 0 modules Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 355311: cache has only 0 modules 13%|█▎ | 160/1208 [2:18:27<12:28:15, 42.84s/it] {'loss': 0.0013, 'grad_norm': 7.853790294429239, 'learning_rate': 8.675496688741721e-07, 'completion_length': 100.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.03277587890625, 'clip_ratio': 0.0, 'epoch': 1.06} 13%|█▎ | 160/1208 [2:18:27<12:28:15, 42.84s/it]Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 356184: cache has only 0 modules Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 357057: cache has only 0 modules 13%|█▎ | 161/1208 [2:19:26<13:52:36, 47.71s/it] {'loss': 0.0023, 'grad_norm': 5.854588376789489, 'learning_rate': 8.667218543046357e-07, 'completion_length': 124.0, 'rewards/accuracy_reward_action': 0.8125, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 0.875, 'reward': 2.0, 'reward_std': 0.9910312294960022, 'kl': 0.058349609375, 'clip_ratio': 0.0, 'epoch': 1.07} 13%|█▎ | 161/1208 [2:19:26<13:52:36, 47.71s/it]Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 357930: cache has only 0 modules Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 358803: cache has only 0 modules 13%|█▎ | 162/1208 [2:20:02<12:52:53, 44.33s/it] {'loss': 0.0012, 'grad_norm': 5.2969210415162715, 'learning_rate': 8.658940397350994e-07, 'completion_length': 89.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.02960205078125, 'clip_ratio': 0.0, 'epoch': 1.07} 13%|█▎ | 162/1208 [2:20:02<12:52:53, 44.33s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 359676: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 360549: cache has only 0 modules 13%|█▎ | 163/1208 [2:20:49<13:03:00, 44.96s/it] {'loss': 0.0028, 'grad_norm': 5.967154503066607, 'learning_rate': 8.650662251655628e-07, 'completion_length': 106.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.069091796875, 'clip_ratio': 0.0, 'epoch': 1.08} 13%|█▎ | 163/1208 [2:20:49<13:03:00, 44.96s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 361422: cache has only 0 modules Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 362295: cache has only 0 modules 14%|█▎ | 164/1208 [2:21:26<12:21:06, 42.59s/it] {'loss': 0.0464, 'grad_norm': 35.53489753269246, 'learning_rate': 8.642384105960264e-07, 'completion_length': 90.8125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.5487885922193527, 'kl': 1.1588134765625, 'clip_ratio': 0.0, 'epoch': 1.09} 14%|█▎ | 164/1208 [2:21:26<12:21:06, 42.59s/it]Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 363168: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 364041: cache has only 0 modules [Step 164] loss_orig = 0.002413, loss_refine = 2.476683[Step 164] loss_orig = 0.001340, loss_refine = -0.351657 [Step 164] loss_orig = 0.001168, loss_refine = -0.352475 [Step 164] loss_orig = 0.002933, loss_refine = -0.351768 [Step 164] loss_orig = 0.001488, loss_refine = -0.352416 [Step 164] loss_orig = 0.000984, loss_refine = -0.351988 [Step 164] loss_orig = 0.001278, loss_refine = -0.351407 [Step 164] loss_orig = 0.001943, loss_refine = -0.352129 Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 364914: cache has only 0 modules 14%|█▎ | 165/1208 [2:22:20<13:20:41, 46.06s/it] {'loss': 0.0012, 'grad_norm': 8.557236966040591, 'learning_rate': 8.634105960264901e-07, 'completion_length': 94.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.03192138671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 1.09} 14%|█▎ | 165/1208 [2:22:20<13:20:41, 46.06s/it]Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 365787: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 366660: cache has only 0 modules 14%|█▎ | 166/1208 [2:22:58<12:40:42, 43.80s/it] {'loss': 0.0011, 'grad_norm': 0.27207211365161843, 'learning_rate': 8.625827814569536e-07, 'completion_length': 90.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02667236328125, 'clip_ratio': 0.0, 'epoch': 1.1} 14%|█▎ | 166/1208 [2:22:58<12:40:42, 43.80s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 367533: cache has only 0 modules Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 368406: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 369279: cache has only 0 modules [Step 166] loss_orig = 0.000611, loss_refine = -0.539542[Step 166] loss_orig = 0.001169, loss_refine = 1.621329 [Step 166] loss_orig = 0.001528, loss_refine = -0.539490 [Step 166] loss_orig = 0.000704, loss_refine = -0.537273[Step 166] loss_orig = 0.000581, loss_refine = -0.537326 [Step 166] loss_orig = 0.000735, loss_refine = 1.620571 [Step 166] loss_orig = 0.002812, loss_refine = -0.537860 [Step 166] loss_orig = 0.000928, loss_refine = -0.538906 14%|█▍ | 167/1208 [2:23:53<13:38:16, 47.16s/it] {'loss': 0.0015, 'grad_norm': 5.834370050297313, 'learning_rate': 8.617549668874172e-07, 'completion_length': 89.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.11} 14%|█▍ | 167/1208 [2:23:53<13:38:16, 47.16s/it]Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 370152: cache has only 0 modules Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 371025: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'cancel subscription'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 371898: cache has only 0 modules [Step 167] loss_orig = -0.351282, loss_refine = 0.542743[Step 167] loss_orig = 2.476963, loss_refine = -0.538765[Step 167] loss_orig = -0.352448, loss_refine = 0.541012 [Step 167] loss_orig = -0.352259, loss_refine = 1.621313 [Step 167] loss_orig = -0.352709, loss_refine = -0.538511 [Step 167] loss_orig = -0.349142, loss_refine = -0.538891 [Step 167] loss_orig = -0.352089, loss_refine = 0.541568 [Step 167] loss_orig = -0.352899, loss_refine = -1.618620 14%|█▍ | 168/1208 [2:24:48<14:16:52, 49.44s/it] {'loss': 0.0012, 'grad_norm': 12.893791882655314, 'learning_rate': 8.609271523178807e-07, 'completion_length': 94.66666666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.25, 'reward_std': 0.7224831183751425, 'kl': 0.0345458984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.11} 14%|█▍ | 168/1208 [2:24:48<14:16:52, 49.44s/it]Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 372771: cache has only 0 modules Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 373644: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sign in - Google Accounts'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 374517: cache has only 0 modules [Step 168] loss_orig = 0.001951, loss_refine = -0.503266[Step 168] loss_orig = 0.001207, loss_refine = -0.495137 [Step 168] loss_orig = 0.001266, loss_refine = -0.502750 [Step 168] loss_orig = 0.001856, loss_refine = -0.502813 [Step 168] loss_orig = 0.001235, loss_refine = -0.502477 [Step 168] loss_orig = 0.000945, loss_refine = 0.843150 [Step 168] loss_orig = 0.001133, loss_refine = -0.502269 [Step 168] loss_orig = 0.001343, loss_refine = 2.186180 14%|█▍ | 169/1208 [2:25:47<15:04:09, 52.21s/it] {'loss': 0.0018, 'grad_norm': 11.126147561113125, 'learning_rate': 8.600993377483444e-07, 'completion_length': 96.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.4205243190129598, 'kl': 0.02960205078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 1.12} 14%|█▍ | 169/1208 [2:25:47<15:04:09, 52.21s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 375390: cache has only 0 modules Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 376263: cache has only 0 modules 14%|█▍ | 170/1208 [2:26:27<14:01:23, 48.64s/it] {'loss': 0.0019, 'grad_norm': 13.822619154359492, 'learning_rate': 8.592715231788079e-07, 'completion_length': 87.4375, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.9023419618606567, 'kl': 0.0465087890625, 'clip_ratio': 0.0, 'epoch': 1.13} 14%|█▍ | 170/1208 [2:26:27<14:01:23, 48.64s/it]Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 377136: cache has only 0 modules Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 378009: cache has only 0 modules 14%|█▍ | 171/1208 [2:27:04<13:00:31, 45.16s/it] {'loss': 0.0013, 'grad_norm': 0.27157603191202123, 'learning_rate': 8.584437086092715e-07, 'completion_length': 97.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03302001953125, 'clip_ratio': 0.0, 'epoch': 1.13} 14%|█▍ | 171/1208 [2:27:04<13:00:31, 45.16s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 378882: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1394, 608]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 379755: cache has only 0 modules [Step 171] loss_orig = 0.001246, loss_refine = 0.546452[Step 171] loss_orig = 0.000942, loss_refine = 0.541764 [Step 171] loss_orig = 0.001588, loss_refine = -1.618634 [Step 171] loss_orig = 0.001414, loss_refine = 0.541898 [Step 171] loss_orig = 0.001725, loss_refine = 0.540964 [Step 171] loss_orig = 0.001468, loss_refine = 0.541520 [Step 171] loss_orig = 0.001434, loss_refine = -1.617298 [Step 171] loss_orig = 0.003005, loss_refine = 0.541390 Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 380628: cache has only 0 modules 14%|█▍ | 172/1208 [2:28:03<14:08:47, 49.16s/it] {'loss': 0.0017, 'grad_norm': 21.121431576739088, 'learning_rate': 8.576158940397351e-07, 'completion_length': 107.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.33247750997543335, 'kl': 0.034912109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.14} 14%|█▍ | 172/1208 [2:28:03<14:08:47, 49.16s/it]Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 381501: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 382374: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [858, 85]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 383247: cache has only 0 modules [Step 172] loss_orig = 0.001723, loss_refine = -1.205720[Step 172] loss_orig = 0.002309, loss_refine = 0.726065[Step 172] loss_orig = 0.000890, loss_refine = 0.725577[Step 172] loss_orig = 0.000562, loss_refine = -1.206691[Step 172] loss_orig = 0.000842, loss_refine = 0.725552[Step 172] loss_orig = 0.000771, loss_refine = 0.725212 [Step 172] loss_orig = 0.001264, loss_refine = 0.726299[Step 172] loss_orig = 0.000531, loss_refine = -1.205760 14%|█▍ | 173/1208 [2:28:52<14:07:52, 49.15s/it] {'loss': 0.0019, 'grad_norm': 11.884892333679408, 'learning_rate': 8.567880794701986e-07, 'completion_length': 94.83333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4205243190129598, 'kl': 0.044677734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 1.15} 14%|█▍ | 173/1208 [2:28:52<14:07:52, 49.15s/it]Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 384120: cache has only 0 modules Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 384993: cache has only 0 modules 14%|█▍ | 174/1208 [2:29:33<13:29:02, 46.95s/it] {'loss': 0.001, 'grad_norm': 4.430263362054275, 'learning_rate': 8.559602649006622e-07, 'completion_length': 92.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.02459716796875, 'clip_ratio': 0.0, 'epoch': 1.15} 14%|█▍ | 174/1208 [2:29:33<13:29:02, 46.95s/it]Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 385866: cache has only 0 modules Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 386739: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 387612: cache has only 0 modules [Step 174] loss_orig = 0.000860, loss_refine = 1.049270[Step 174] loss_orig = 0.000801, loss_refine = -0.148821 [Step 174] loss_orig = 0.001106, loss_refine = 1.050726 [Step 174] loss_orig = 0.000889, loss_refine = -1.346654 [Step 174] loss_orig = 0.000885, loss_refine = -1.344805 [Step 174] loss_orig = 0.001527, loss_refine = -0.147250 [Step 174] loss_orig = 0.001417, loss_refine = 1.050299 [Step 174] loss_orig = 0.001562, loss_refine = -0.148586 14%|█▍ | 175/1208 [2:30:25<13:50:51, 48.26s/it] {'loss': 0.0014, 'grad_norm': 7.076637480007595, 'learning_rate': 8.551324503311258e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.27817432085673016, 'kl': 0.02703857421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.16} 14%|█▍ | 175/1208 [2:30:25<13:50:51, 48.26s/it]Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 388485: cache has only 0 modules Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 389358: cache has only 0 modules 15%|█▍ | 176/1208 [2:31:06<13:14:41, 46.20s/it] {'loss': 0.0017, 'grad_norm': 7.788054155757547, 'learning_rate': 8.543046357615895e-07, 'completion_length': 109.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.49022960662841797, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 1.17} 15%|█▍ | 176/1208 [2:31:06<13:14:41, 46.20s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 390231: cache has only 0 modules Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 391104: cache has only 0 modules 15%|█▍ | 177/1208 [2:31:47<12:43:39, 44.44s/it] {'loss': 0.0018, 'grad_norm': 3.7741587996621635, 'learning_rate': 8.534768211920529e-07, 'completion_length': 90.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0438232421875, 'clip_ratio': 0.0, 'epoch': 1.17} 15%|█▍ | 177/1208 [2:31:47<12:43:39, 44.44s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 391977: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element LibreOffice Writer'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 392850: cache has only 0 modules [Step 177] loss_orig = 0.000691, loss_refine = -0.537903 [Step 177] loss_orig = 0.000892, loss_refine = 1.621191[Step 177] loss_orig = 0.001040, loss_refine = -0.538454 [Step 177] loss_orig = 0.001709, loss_refine = -0.539234 [Step 177] loss_orig = 0.002241, loss_refine = -0.539030[Step 177] loss_orig = 0.003015, loss_refine = -0.538680 [Step 177] loss_orig = 0.001191, loss_refine = 1.623151 [Step 177] loss_orig = 0.000641, loss_refine = -0.538924 Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 393723: cache has only 0 modules 15%|█▍ | 178/1208 [2:32:47<14:03:22, 49.13s/it] {'loss': 0.0014, 'grad_norm': 13.001292129799431, 'learning_rate': 8.526490066225165e-07, 'completion_length': 92.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.03326416015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.18} 15%|█▍ | 178/1208 [2:32:47<14:03:22, 49.13s/it]Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 394596: cache has only 0 modules Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 395469: cache has only 0 modules 15%|█▍ | 179/1208 [2:33:22<12:53:37, 45.11s/it] {'loss': 0.0019, 'grad_norm': 10.07831549701619, 'learning_rate': 8.518211920529801e-07, 'completion_length': 81.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.04833984375, 'clip_ratio': 0.0, 'epoch': 1.19} 15%|█▍ | 179/1208 [2:33:22<12:53:37, 45.11s/it]Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 396342: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 397215: cache has only 0 modules 15%|█▍ | 180/1208 [2:34:00<12:16:32, 42.99s/it] {'loss': 0.0014, 'grad_norm': 6.296683814348399, 'learning_rate': 8.509933774834437e-07, 'completion_length': 93.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0345458984375, 'clip_ratio': 0.0, 'epoch': 1.19} 15%|█▍ | 180/1208 [2:34:00<12:16:32, 42.99s/it]Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 398088: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functions'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 398961: cache has only 0 modules [Step 180] loss_orig = 0.001483, loss_refine = 0.541351[Step 180] loss_orig = 0.000463, loss_refine = 0.542043 [Step 180] loss_orig = 0.013698, loss_refine = -1.617787 [Step 180] loss_orig = 0.001531, loss_refine = 0.541053[Step 180] loss_orig = 0.002155, loss_refine = -1.617609 [Step 180] loss_orig = 0.000793, loss_refine = 0.541229 [Step 180] loss_orig = 0.001333, loss_refine = 0.542808 [Step 180] loss_orig = 0.006917, loss_refine = 0.540955 Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 399834: cache has only 0 modules 15%|█▍ | 181/1208 [2:35:05<14:05:47, 49.41s/it] {'loss': 0.002, 'grad_norm': 8.183757324523897, 'learning_rate': 8.501655629139073e-07, 'completion_length': 100.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.30860670407613117, 'kl': 0.0721435546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.2} 15%|█▍ | 181/1208 [2:35:05<14:05:47, 49.41s/it]Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 400707: cache has only 0 modules Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 401580: cache has only 0 modules 15%|█▌ | 182/1208 [2:35:39<12:45:23, 44.76s/it] {'loss': 0.0015, 'grad_norm': 15.079495945127281, 'learning_rate': 8.493377483443708e-07, 'completion_length': 94.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.7071067541837692, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'epoch': 1.21} 15%|█▌ | 182/1208 [2:35:39<12:45:23, 44.76s/it]Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 402453: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 403326: cache has only 0 modules 15%|█▌ | 183/1208 [2:36:14<11:58:33, 42.06s/it] {'loss': 0.0015, 'grad_norm': 5.382028746213343, 'learning_rate': 8.485099337748343e-07, 'completion_length': 91.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 1.21} 15%|█▌ | 183/1208 [2:36:14<11:58:33, 42.06s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 404199: cache has only 0 modules Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 405072: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 405945: cache has only 0 modules [Step 183] loss_orig = 0.001623, loss_refine = 0.001090[Step 183] loss_orig = 0.001981, loss_refine = 0.003056[Step 183] loss_orig = 0.001803, loss_refine = 0.001023[Step 183] loss_orig = 0.001703, loss_refine = 0.004585 [Step 183] loss_orig = 0.002056, loss_refine = 0.002404[Step 183] loss_orig = 0.001626, loss_refine = 0.002205[Step 183] loss_orig = 0.001213, loss_refine = 0.002781 [Step 183] loss_orig = 0.001171, loss_refine = 0.001737 15%|█▌ | 184/1208 [2:37:08<12:58:07, 45.59s/it] {'loss': 0.0027, 'grad_norm': 13.426296413673318, 'learning_rate': 8.47682119205298e-07, 'completion_length': 84.08333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4166666666666665, 'reward_std': 0.3450327714284261, 'kl': 0.0587158203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.22} 15%|█▌ | 184/1208 [2:37:08<12:58:07, 45.59s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 406818: cache has only 0 modules Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 407691: cache has only 0 modules 15%|█▌ | 185/1208 [2:37:58<13:19:23, 46.89s/it] {'loss': 0.0015, 'grad_norm': 6.13691350869866, 'learning_rate': 8.468543046357616e-07, 'completion_length': 108.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.036865234375, 'clip_ratio': 0.0, 'epoch': 1.23} 15%|█▌ | 185/1208 [2:37:58<13:19:23, 46.89s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 408564: cache has only 0 modules Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 409437: cache has only 0 modules 15%|█▌ | 186/1208 [2:38:42<13:04:00, 46.03s/it] {'loss': 0.0017, 'grad_norm': 4.101998521568941, 'learning_rate': 8.460264900662252e-07, 'completion_length': 111.8125, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.3204349875450134, 'kl': 0.04296875, 'clip_ratio': 0.0, 'epoch': 1.23} 15%|█▌ | 186/1208 [2:38:42<13:04:00, 46.03s/it]Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 410310: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 411183: cache has only 0 modules 15%|█▌ | 187/1208 [2:39:31<13:19:24, 46.98s/it] {'loss': 0.0021, 'grad_norm': 12.837912153235616, 'learning_rate': 8.451986754966886e-07, 'completion_length': 106.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0511474609375, 'clip_ratio': 0.0, 'epoch': 1.24} 15%|█▌ | 187/1208 [2:39:31<13:19:24, 46.98s/it]Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 412056: cache has only 0 modules Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 412929: cache has only 0 modules 16%|█▌ | 188/1208 [2:40:13<12:51:26, 45.38s/it] {'loss': 0.0026, 'grad_norm': 5.413292258792196, 'learning_rate': 8.443708609271523e-07, 'completion_length': 102.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.06396484375, 'clip_ratio': 0.0, 'epoch': 1.25} 16%|█▌ | 188/1208 [2:40:13<12:51:26, 45.38s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 413802: cache has only 0 modules Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 414675: cache has only 0 modules 16%|█▌ | 189/1208 [2:41:03<13:15:49, 46.86s/it] {'loss': 0.0018, 'grad_norm': 15.570297253299692, 'learning_rate': 8.435430463576159e-07, 'completion_length': 120.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 0.9375, 'reward': 2.0625, 'reward_std': 0.6396867483854294, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'epoch': 1.25} 16%|█▌ | 189/1208 [2:41:03<13:15:49, 46.86s/it]Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 415548: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 416421: cache has only 0 modules [Step 189] loss_orig = 0.002323, loss_refine = -0.539447[Step 189] loss_orig = 0.000916, loss_refine = -0.539137 [Step 189] loss_orig = 0.000828, loss_refine = 1.620464 [Step 189] loss_orig = 0.002904, loss_refine = -0.539196 [Step 189] loss_orig = 0.000651, loss_refine = 1.622672 [Step 189] loss_orig = 0.000959, loss_refine = -0.539176 [Step 189] loss_orig = 0.000676, loss_refine = -0.539251 [Step 189] loss_orig = 0.001005, loss_refine = -0.539667 Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 417294: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 418167: cache has only 0 modules [Step 189] loss_orig = 2.186785, loss_refine = 0.685146 [Step 189] loss_orig = -0.501409, loss_refine = -0.407892 [Step 189] loss_orig = -0.502182, loss_refine = 0.684392[Step 189] loss_orig = -0.501722, loss_refine = -0.408057 [Step 189] loss_orig = -0.501756, loss_refine = -0.407492 [Step 189] loss_orig = -0.502230, loss_refine = -0.404760 [Step 189] loss_orig = 0.846123, loss_refine = 1.775214 [Step 189] loss_orig = -0.502006, loss_refine = -1.499625 16%|█▌ | 190/1208 [2:42:38<17:19:40, 61.28s/it] {'loss': 0.0015, 'grad_norm': 22.185438895354263, 'learning_rate': 8.427152317880794e-07, 'completion_length': 117.09375, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 0.90625, 'reward': 2.1875, 'reward_std': 0.6464923322200775, 'kl': 0.0496826171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 1.26} 16%|█▌ | 190/1208 [2:42:38<17:19:40, 61.28s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 419040: cache has only 0 modules Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 419913: cache has only 0 modules 16%|█▌ | 191/1208 [2:43:25<16:04:42, 56.92s/it] {'loss': 0.0015, 'grad_norm': 5.190963389272751, 'learning_rate': 8.418874172185431e-07, 'completion_length': 96.125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.5625, 'reward_std': 0.7975912988185883, 'kl': 0.0382080078125, 'clip_ratio': 0.0, 'epoch': 1.26} 16%|█▌ | 191/1208 [2:43:25<16:04:42, 56.92s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 420786: cache has only 0 modules Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 421659: cache has only 0 modules 16%|█▌ | 192/1208 [2:44:04<14:33:27, 51.58s/it] {'loss': 0.0013, 'grad_norm': 6.304703443468346, 'learning_rate': 8.410596026490066e-07, 'completion_length': 94.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.033203125, 'clip_ratio': 0.0, 'epoch': 1.27} 16%|█▌ | 192/1208 [2:44:04<14:33:27, 51.58s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 422532: cache has only 0 modules Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 423405: cache has only 0 modules 16%|█▌ | 193/1208 [2:44:41<13:18:58, 47.23s/it] {'loss': 0.0013, 'grad_norm': 0.2146551315204457, 'learning_rate': 8.402317880794701e-07, 'completion_length': 87.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03125, 'clip_ratio': 0.0, 'epoch': 1.28} 16%|█▌ | 193/1208 [2:44:41<13:18:58, 47.23s/it]Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 424278: cache has only 0 modules Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 425151: cache has only 0 modules 16%|█▌ | 194/1208 [2:45:26<13:04:24, 46.41s/it] {'loss': 0.0015, 'grad_norm': 8.19164357967878, 'learning_rate': 8.394039735099337e-07, 'completion_length': 100.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'epoch': 1.28} 16%|█▌ | 194/1208 [2:45:26<13:04:24, 46.41s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 426024: cache has only 0 modules Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 426897: cache has only 0 modules 16%|█▌ | 195/1208 [2:46:14<13:12:16, 46.93s/it] {'loss': 0.0014, 'grad_norm': 6.840929058844439, 'learning_rate': 8.385761589403974e-07, 'completion_length': 114.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0345458984375, 'clip_ratio': 0.0, 'epoch': 1.29} 16%|█▌ | 195/1208 [2:46:14<13:12:16, 46.93s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 427770: cache has only 0 modules Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 428643: cache has only 0 modules 16%|█▌ | 196/1208 [2:46:50<12:16:55, 43.69s/it] {'loss': 0.0014, 'grad_norm': 4.211332640090985, 'learning_rate': 8.377483443708609e-07, 'completion_length': 99.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.03515625, 'clip_ratio': 0.0, 'epoch': 1.3} 16%|█▌ | 196/1208 [2:46:50<12:16:55, 43.69s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 429516: cache has only 0 modules Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 430389: cache has only 0 modules 16%|█▋ | 197/1208 [2:47:33<12:11:08, 43.39s/it] {'loss': 0.0013, 'grad_norm': 7.102430500407375, 'learning_rate': 8.369205298013244e-07, 'completion_length': 99.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0323486328125, 'clip_ratio': 0.0, 'epoch': 1.3} 16%|█▋ | 197/1208 [2:47:33<12:11:08, 43.39s/it]Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 431262: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 432135: cache has only 0 modules 16%|█▋ | 198/1208 [2:48:12<11:47:57, 42.06s/it] {'loss': 0.0012, 'grad_norm': 3.383264848226272, 'learning_rate': 8.36092715231788e-07, 'completion_length': 93.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0306396484375, 'clip_ratio': 0.0, 'epoch': 1.31} 16%|█▋ | 198/1208 [2:48:12<11:47:57, 42.06s/it]Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 433008: cache has only 0 modules Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 433881: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 434754: cache has only 0 modules [Step 198] loss_orig = 0.000911, loss_refine = 1.985964[Step 198] loss_orig = 0.001368, loss_refine = 0.663102[Step 198] loss_orig = 0.002332, loss_refine = -0.660522 [Step 198] loss_orig = 0.002674, loss_refine = -0.660199 [Step 198] loss_orig = 0.001823, loss_refine = 0.663680 [Step 198] loss_orig = 0.002123, loss_refine = -0.659929 [Step 198] loss_orig = 0.001652, loss_refine = -0.659592 [Step 198] loss_orig = 0.000814, loss_refine = -0.659205 16%|█▋ | 199/1208 [2:49:14<13:30:12, 48.18s/it] {'loss': 0.0015, 'grad_norm': 5.567757281956224, 'learning_rate': 8.352649006622517e-07, 'completion_length': 106.20833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.36982743938763935, 'kl': 0.0377197265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.32} 16%|█▋ | 199/1208 [2:49:14<13:30:12, 48.18s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 435627: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 436500: cache has only 0 modules 17%|█▋ | 200/1208 [2:50:05<13:42:15, 48.94s/it] {'loss': 0.0025, 'grad_norm': 32.053328743222586, 'learning_rate': 8.344370860927152e-07, 'completion_length': 108.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.061767578125, 'clip_ratio': 0.0, 'epoch': 1.32} 17%|█▋ | 200/1208 [2:50:05<13:42:15, 48.94s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 437373: cache has only 0 modules Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 438246: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element System'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 439119: cache has only 0 modules [Step 200] loss_orig = -0.352405, loss_refine = 1.621447[Step 200] loss_orig = 2.505953, loss_refine = -0.538360 [Step 200] loss_orig = -0.352011, loss_refine = -0.538124 [Step 200] loss_orig = -0.351977, loss_refine = 1.624392 [Step 200] loss_orig = -0.351630, loss_refine = -0.538133 [Step 200] loss_orig = -0.349578, loss_refine = -0.538381[Step 200] loss_orig = -0.351714, loss_refine = -0.537029 [Step 200] loss_orig = -0.351717, loss_refine = -0.535623 17%|█▋ | 201/1208 [2:51:10<15:00:45, 53.67s/it] {'loss': 0.0019, 'grad_norm': 5.807995829026725, 'learning_rate': 8.336092715231787e-07, 'completion_length': 104.16666666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.27215448021888733, 'kl': 0.0872802734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 1.33} 17%|█▋ | 201/1208 [2:51:10<15:00:45, 53.67s/it]Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 439992: cache has only 0 modules Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 440865: cache has only 0 modules 17%|█▋ | 202/1208 [2:51:57<14:28:46, 51.82s/it] {'loss': 0.0016, 'grad_norm': 4.6728408956280285, 'learning_rate': 8.327814569536424e-07, 'completion_length': 113.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0408935546875, 'clip_ratio': 0.0, 'epoch': 1.34} 17%|█▋ | 202/1208 [2:51:57<14:28:46, 51.82s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 441738: cache has only 0 modules Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 442611: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 443484: cache has only 0 modules [Step 202] loss_orig = 0.002599, loss_refine = -0.538860 [Step 202] loss_orig = 0.002667, loss_refine = -0.538518 [Step 202] loss_orig = 0.001962, loss_refine = 1.622749[Step 202] loss_orig = 0.001335, loss_refine = -0.538841 [Step 202] loss_orig = 0.001166, loss_refine = -0.538003 [Step 202] loss_orig = 0.001737, loss_refine = 1.621232 [Step 202] loss_orig = 0.001719, loss_refine = -0.538659 [Step 202] loss_orig = 0.001558, loss_refine = -0.538879 17%|█▋ | 203/1208 [2:53:03<15:38:14, 56.01s/it] {'loss': 0.0021, 'grad_norm': 8.001326109315757, 'learning_rate': 8.31953642384106e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.30860670407613117, 'kl': 0.0552978515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.34} 17%|█▋ | 203/1208 [2:53:03<15:38:14, 56.01s/it]Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 444357: cache has only 0 modules Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 445230: cache has only 0 modules 17%|█▋ | 204/1208 [2:53:40<14:04:53, 50.49s/it] {'loss': 0.0013, 'grad_norm': 6.361222018375982, 'learning_rate': 8.311258278145695e-07, 'completion_length': 97.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03253173828125, 'clip_ratio': 0.0, 'epoch': 1.35} 17%|█▋ | 204/1208 [2:53:40<14:04:53, 50.49s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 446103: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 446976: cache has only 0 modules [Step 204] loss_orig = 0.001171, loss_refine = -1.362768 [Step 204] loss_orig = 0.001458, loss_refine = 0.199819 [Step 204] loss_orig = 0.001040, loss_refine = -1.363899[Step 204] loss_orig = 0.001251, loss_refine = 0.196948 [Step 204] loss_orig = 0.001531, loss_refine = 0.196464 [Step 204] loss_orig = 0.001358, loss_refine = 0.196681 [Step 204] loss_orig = 0.001061, loss_refine = 1.757586 [Step 204] loss_orig = 0.001013, loss_refine = 0.196301 Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 447849: cache has only 0 modules 17%|█▋ | 205/1208 [2:54:40<14:47:47, 53.11s/it] {'loss': 0.0024, 'grad_norm': 10.917044554206381, 'learning_rate': 8.302980132450331e-07, 'completion_length': 91.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.21362332503000894, 'kl': 0.04888916015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.36} 17%|█▋ | 205/1208 [2:54:40<14:47:47, 53.11s/it]Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 448722: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 449595: cache has only 0 modules 17%|█▋ | 206/1208 [2:55:19<13:35:50, 48.85s/it] {'loss': 0.0018, 'grad_norm': 4.643459947626, 'learning_rate': 8.294701986754967e-07, 'completion_length': 88.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.044921875, 'clip_ratio': 0.0, 'epoch': 1.36} 17%|█▋ | 206/1208 [2:55:19<13:35:50, 48.85s/it]Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 450468: cache has only 0 modules Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 451341: cache has only 0 modules 17%|█▋ | 207/1208 [2:55:58<12:45:39, 45.89s/it] {'loss': 0.0013, 'grad_norm': 7.61034817120324, 'learning_rate': 8.286423841059602e-07, 'completion_length': 90.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.49022960662841797, 'kl': 0.03277587890625, 'clip_ratio': 0.0, 'epoch': 1.37} 17%|█▋ | 207/1208 [2:55:58<12:45:39, 45.89s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 452214: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 453087: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Header & Footer...'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 453960: cache has only 0 modules [Step 207] loss_orig = -0.350895, loss_refine = 0.542080[Step 207] loss_orig = -0.350128, loss_refine = 0.543527[Step 207] loss_orig = -0.350863, loss_refine = -1.618916[Step 207] loss_orig = -0.351618, loss_refine = 0.541286 [Step 207] loss_orig = -0.351905, loss_refine = 0.541383 [Step 207] loss_orig = -0.351935, loss_refine = 0.541774[Step 207] loss_orig = 2.484034, loss_refine = 0.544476[Step 207] loss_orig = -0.351846, loss_refine = -1.618217 17%|█▋ | 208/1208 [2:56:50<13:19:47, 47.99s/it] {'loss': 0.0021, 'grad_norm': 10.442542940123058, 'learning_rate': 8.278145695364238e-07, 'completion_length': 89.79166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.27215448021888733, 'kl': 0.06298828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.38} 17%|█▋ | 208/1208 [2:56:50<13:19:47, 47.99s/it]Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 454833: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 455706: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Channel watermark'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 456579: cache has only 0 modules [Step 208] loss_orig = 0.005435, loss_refine = -0.350164[Step 208] loss_orig = 0.002270, loss_refine = -0.352355[Step 208] loss_orig = 0.001386, loss_refine = 2.478894 [Step 208] loss_orig = 0.002223, loss_refine = -0.352278 [Step 208] loss_orig = 0.001155, loss_refine = -0.352510 [Step 208] loss_orig = 0.001481, loss_refine = -0.351567 [Step 208] loss_orig = 0.001298, loss_refine = -0.351987 [Step 208] loss_orig = 0.001943, loss_refine = -0.352802 17%|█▋ | 209/1208 [2:57:51<14:21:45, 51.76s/it] {'loss': 0.002, 'grad_norm': 7.95257551605925, 'learning_rate': 8.269867549668874e-07, 'completion_length': 91.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.0531005859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 1.38} 17%|█▋ | 209/1208 [2:57:51<14:21:45, 51.76s/it]Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 457452: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open photo'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 458325: cache has only 0 modules [Step 209] loss_orig = 0.002177, loss_refine = -1.618909[Step 209] loss_orig = 0.002252, loss_refine = 0.541765[Step 209] loss_orig = 0.000932, loss_refine = 0.540543 [Step 209] loss_orig = 0.018042, loss_refine = 0.541256 [Step 209] loss_orig = 0.000818, loss_refine = 0.540495 [Step 209] loss_orig = 0.001100, loss_refine = 0.541568 [Step 209] loss_orig = 0.001646, loss_refine = 0.548911 [Step 209] loss_orig = 0.002185, loss_refine = -1.618574 Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 459198: cache has only 0 modules 17%|█▋ | 210/1208 [2:58:44<14:25:48, 52.05s/it] {'loss': 0.002, 'grad_norm': 7.71591360618888, 'learning_rate': 8.261589403973509e-07, 'completion_length': 91.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3268197377522786, 'kl': 0.0697021484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.39} 17%|█▋ | 210/1208 [2:58:44<14:25:48, 52.05s/it]Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 460071: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more settings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 460944: cache has only 0 modules [Step 210] loss_orig = 0.004764, loss_refine = 0.356088[Step 210] loss_orig = 0.002953, loss_refine = 0.358401[Step 210] loss_orig = 0.003471, loss_refine = -2.473157[Step 210] loss_orig = 0.003243, loss_refine = 0.356177 [Step 210] loss_orig = 0.001421, loss_refine = 0.355216 [Step 210] loss_orig = 0.002566, loss_refine = 0.355974 [Step 210] loss_orig = 0.001412, loss_refine = 0.354906 [Step 210] loss_orig = 0.001055, loss_refine = 0.356918 Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 461817: cache has only 0 modules 17%|█▋ | 211/1208 [2:59:40<14:43:38, 53.18s/it] {'loss': 0.0024, 'grad_norm': 4.223408630916815, 'learning_rate': 8.253311258278145e-07, 'completion_length': 93.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.0611572265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 1.4} 17%|█▋ | 211/1208 [2:59:40<14:43:38, 53.18s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 462690: cache has only 0 modules Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 463563: cache has only 0 modules 18%|█▊ | 212/1208 [3:00:20<13:37:04, 49.22s/it] {'loss': 0.0043, 'grad_norm': 7.694698048543859, 'learning_rate': 8.245033112582781e-07, 'completion_length': 95.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.6307864785194397, 'kl': 0.107666015625, 'clip_ratio': 0.0, 'epoch': 1.4} 18%|█▊ | 212/1208 [3:00:20<13:37:04, 49.22s/it]Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 464436: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command '1'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 465309: cache has only 0 modules [Step 212] loss_orig = 0.002106, loss_refine = 0.936296[Step 212] loss_orig = 0.001514, loss_refine = 0.938120[Step 212] loss_orig = 0.001823, loss_refine = 0.936270 [Step 212] loss_orig = 0.002755, loss_refine = 0.936155 [Step 212] loss_orig = 0.001665, loss_refine = -0.932410 [Step 212] loss_orig = 0.001683, loss_refine = -0.933207[Step 212] loss_orig = 0.001866, loss_refine = -0.932944 [Step 212] loss_orig = 0.001893, loss_refine = -0.933377 Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 466182: cache has only 0 modules 18%|█▊ | 213/1208 [3:01:07<13:29:07, 48.79s/it] {'loss': 0.0016, 'grad_norm': 7.588259088411861, 'learning_rate': 8.236754966887416e-07, 'completion_length': 93.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.33247750997543335, 'kl': 0.0406494140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.41} 18%|█▊ | 213/1208 [3:01:07<13:29:07, 48.79s/it]Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 467055: cache has only 0 modules Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 467928: cache has only 0 modules 18%|█▊ | 214/1208 [3:01:38<11:59:18, 43.42s/it] {'loss': 0.0016, 'grad_norm': 9.817024775113502, 'learning_rate': 8.228476821192053e-07, 'completion_length': 84.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.03973388671875, 'clip_ratio': 0.0, 'epoch': 1.42} 18%|█▊ | 214/1208 [3:01:38<11:59:18, 43.42s/it]Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 468801: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [353, 623]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 469674: cache has only 0 modules [Step 214] loss_orig = 0.001063, loss_refine = 0.847140 [Step 214] loss_orig = 0.001188, loss_refine = 0.847968 [Step 214] loss_orig = 0.002287, loss_refine = 0.847227 [Step 214] loss_orig = 0.001806, loss_refine = -0.280076 [Step 214] loss_orig = 0.001047, loss_refine = 0.847129[Step 214] loss_orig = 0.001426, loss_refine = -1.407259 [Step 214] loss_orig = 0.001439, loss_refine = -0.280463 [Step 214] loss_orig = 0.001389, loss_refine = -1.408697 Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 470547: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 471420: cache has only 0 modules [Step 214] loss_orig = 0.003675, loss_refine = -0.537819[Step 214] loss_orig = 0.000721, loss_refine = 1.623644[Step 214] loss_orig = 0.002063, loss_refine = -0.538655[Step 214] loss_orig = 0.003948, loss_refine = -0.538303 [Step 214] loss_orig = 0.001424, loss_refine = 1.621667 [Step 214] loss_orig = 0.001963, loss_refine = -0.537320 [Step 214] loss_orig = 0.001318, loss_refine = -0.538759[Step 214] loss_orig = 0.001371, loss_refine = -0.538281 18%|█▊ | 215/1208 [3:02:49<14:13:17, 51.56s/it] {'loss': 0.0018, 'grad_norm': 21.575581148951432, 'learning_rate': 8.220198675496688e-07, 'completion_length': 93.59375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.33732882142066956, 'kl': 0.0439453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.42} 18%|█▊ | 215/1208 [3:02:49<14:13:17, 51.56s/it]Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 472293: cache has only 0 modules Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 473166: cache has only 0 modules 18%|█▊ | 216/1208 [3:03:22<12:41:01, 46.03s/it] {'loss': 0.0073, 'grad_norm': 18.571802805868, 'learning_rate': 8.211920529801324e-07, 'completion_length': 87.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.1822509765625, 'clip_ratio': 0.0, 'epoch': 1.43} 18%|█▊ | 216/1208 [3:03:22<12:41:01, 46.03s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 474039: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Format'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 474912: cache has only 0 modules [Step 216] loss_orig = -0.352783, loss_refine = -0.722439[Step 216] loss_orig = -0.351497, loss_refine = -0.722388 [Step 216] loss_orig = -0.351895, loss_refine = 1.209542[Step 216] loss_orig = 2.476644, loss_refine = -0.723030 [Step 216] loss_orig = -0.351611, loss_refine = 1.214405[Step 216] loss_orig = -0.350911, loss_refine = -0.722673 [Step 216] loss_orig = -0.352247, loss_refine = -0.722636 [Step 216] loss_orig = -0.352061, loss_refine = 1.209272 Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 475785: cache has only 0 modules 18%|█▊ | 217/1208 [3:04:26<14:07:51, 51.33s/it] {'loss': 0.0018, 'grad_norm': 6.17172669728573, 'learning_rate': 8.20364238410596e-07, 'completion_length': 109.5, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4583333333333335, 'reward_std': 0.4082186420758565, 'kl': 0.03460693359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.44} 18%|█▊ | 217/1208 [3:04:26<14:07:51, 51.33s/it]Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 476658: cache has only 0 modules Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 477531: cache has only 0 modules 18%|█▊ | 218/1208 [3:05:00<12:44:06, 46.31s/it] {'loss': 0.0028, 'grad_norm': 4.375774879653922, 'learning_rate': 8.195364238410596e-07, 'completion_length': 86.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.071044921875, 'clip_ratio': 0.0, 'epoch': 1.44} 18%|█▊ | 218/1208 [3:05:00<12:44:06, 46.31s/it]Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 478404: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Tray Input Indicator - Chinese (Simplified, China)'. Your previous answer was [{'action': 'click', 'coordinate': [2408, 1371]}> which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 479277: cache has only 0 modules [Step 218] loss_orig = 0.001375, loss_refine = 0.355508 [Step 218] loss_orig = 0.001794, loss_refine = 0.355078[Step 218] loss_orig = 0.001105, loss_refine = 0.355368 [Step 218] loss_orig = 0.001065, loss_refine = -2.472713 [Step 218] loss_orig = 0.001559, loss_refine = 0.356107 [Step 218] loss_orig = 0.001585, loss_refine = 0.354246 [Step 218] loss_orig = 0.001320, loss_refine = 0.355200 [Step 218] loss_orig = 0.006735, loss_refine = 0.356167 Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 480150: cache has only 0 modules 18%|█▊ | 219/1208 [3:06:04<14:08:00, 51.45s/it] {'loss': 0.0017, 'grad_norm': 11.79305103900424, 'learning_rate': 8.187086092715232e-07, 'completion_length': 108.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.2903675138950348, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 1.45} 18%|█▊ | 219/1208 [3:06:04<14:08:00, 51.45s/it]Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 481023: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'use airplay'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 481896: cache has only 0 modules [Step 219] loss_orig = 0.002218, loss_refine = 0.003470[Step 219] loss_orig = 0.003672, loss_refine = 0.002264[Step 219] loss_orig = 0.001232, loss_refine = 0.004131[Step 219] loss_orig = 0.002067, loss_refine = 0.001579 [Step 219] loss_orig = 0.001716, loss_refine = 0.001595 [Step 219] loss_orig = 0.001214, loss_refine = 0.001044 [Step 219] loss_orig = 0.001550, loss_refine = 0.001457 [Step 219] loss_orig = 0.001762, loss_refine = 0.002661 Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 482769: cache has only 0 modules 18%|█▊ | 220/1208 [3:07:00<14:30:14, 52.85s/it] {'loss': 0.0022, 'grad_norm': 5.544715394691081, 'learning_rate': 8.178807947019866e-07, 'completion_length': 90.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.0506591796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.46} 18%|█▊ | 220/1208 [3:07:00<14:30:14, 52.85s/it]Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 483642: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 484515: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Show translate options'. Your previous answer was [{'action': 'click', 'coordinate': [2332, 120]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 485388: cache has only 0 modules [Step 220] loss_orig = 0.001893, loss_refine = 0.936656[Step 220] loss_orig = 0.006518, loss_refine = -0.933609 [Step 220] loss_orig = 0.003167, loss_refine = -0.933721 [Step 220] loss_orig = 0.001268, loss_refine = -0.933630 [Step 220] loss_orig = 0.002840, loss_refine = -0.932968 [Step 220] loss_orig = 0.001723, loss_refine = 0.937026 [Step 220] loss_orig = 0.001924, loss_refine = 0.936400 [Step 220] loss_orig = 0.003287, loss_refine = 0.936691 18%|█▊ | 221/1208 [3:07:54<14:35:05, 53.20s/it] {'loss': 0.0018, 'grad_norm': 18.4783961036078, 'learning_rate': 8.170529801324503e-07, 'completion_length': 93.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.33247750997543335, 'kl': 0.0596923828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.46} 18%|█▊ | 221/1208 [3:07:54<14:35:05, 53.20s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 486261: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 487134: cache has only 0 modules [Step 221] loss_orig = 0.001106, loss_refine = -2.182285[Step 221] loss_orig = 0.003085, loss_refine = -0.838435 [Step 221] loss_orig = 0.001695, loss_refine = 0.504969 [Step 221] loss_orig = 0.001365, loss_refine = 0.509365 [Step 221] loss_orig = 0.000703, loss_refine = 0.507547 [Step 221] loss_orig = 0.000882, loss_refine = 0.505355[Step 221] loss_orig = 0.001525, loss_refine = 0.506114 [Step 221] loss_orig = 0.001978, loss_refine = 0.506573 Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 488007: cache has only 0 modules 18%|█▊ | 222/1208 [3:08:47<14:34:58, 53.24s/it] {'loss': 0.0017, 'grad_norm': 12.527651302358299, 'learning_rate': 8.162251655629139e-07, 'completion_length': 97.04166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.24800793329874674, 'kl': 0.03179931640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.47} 18%|█▊ | 222/1208 [3:08:47<14:34:58, 53.24s/it]Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 488880: cache has only 0 modules Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 489753: cache has only 0 modules 18%|█▊ | 223/1208 [3:09:20<12:55:30, 47.24s/it] {'loss': 0.0021, 'grad_norm': 11.687981775761441, 'learning_rate': 8.153973509933775e-07, 'completion_length': 86.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.0526123046875, 'clip_ratio': 0.0, 'epoch': 1.48} 18%|█▊ | 223/1208 [3:09:20<12:55:30, 47.24s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 490626: cache has only 0 modules Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 491499: cache has only 0 modules 19%|█▊ | 224/1208 [3:09:55<11:50:57, 43.35s/it] {'loss': 0.0013, 'grad_norm': 9.162920753582192, 'learning_rate': 8.14569536423841e-07, 'completion_length': 90.125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.5260358154773712, 'kl': 0.0325927734375, 'clip_ratio': 0.0, 'epoch': 1.48} 19%|█▊ | 224/1208 [3:09:55<11:50:57, 43.35s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 492372: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 493245: cache has only 0 modules [Step 224] loss_orig = -0.351977, loss_refine = -0.537695[Step 224] loss_orig = -0.352033, loss_refine = 1.621777 [Step 224] loss_orig = -0.350396, loss_refine = -0.537814 [Step 224] loss_orig = -0.352249, loss_refine = 1.621128 [Step 224] loss_orig = -0.348226, loss_refine = -0.537245 [Step 224] loss_orig = -0.351388, loss_refine = -0.537110 [Step 224] loss_orig = -0.352069, loss_refine = -0.537515 [Step 224] loss_orig = 2.475240, loss_refine = -0.537406 Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 494118: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 494991: cache has only 0 modules [Step 224] loss_orig = 0.001849, loss_refine = 0.002120[Step 224] loss_orig = 0.000895, loss_refine = 0.001677 [Step 224] loss_orig = 0.000909, loss_refine = 0.001025 [Step 224] loss_orig = 0.000531, loss_refine = 0.001440[Step 224] loss_orig = 0.001087, loss_refine = 0.001222 [Step 224] loss_orig = 0.000959, loss_refine = 0.001076 [Step 224] loss_orig = 0.001038, loss_refine = 0.001892 [Step 224] loss_orig = 0.001304, loss_refine = 0.002827 19%|█▊ | 225/1208 [3:11:15<14:51:07, 54.39s/it] {'loss': 0.002, 'grad_norm': 13.962158628209222, 'learning_rate': 8.137417218543046e-07, 'completion_length': 96.25, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.96875, 'reward': 2.375, 'reward_std': 0.2925042062997818, 'kl': 0.03973388671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 1.49} 19%|█▊ | 225/1208 [3:11:15<14:51:07, 54.39s/it]Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 495864: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Copilot (Ctrl+Shift+.)'. Your previous answer was [{'action': 'click', 'coordinate': [2433, 77]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 496737: cache has only 0 modules [Step 225] loss_orig = 0.005155, loss_refine = 0.002517[Step 225] loss_orig = 0.001374, loss_refine = 0.001481[Step 225] loss_orig = 0.002202, loss_refine = 0.001568 [Step 225] loss_orig = 0.000926, loss_refine = 0.001862 [Step 225] loss_orig = 0.001265, loss_refine = 1.873353 [Step 225] loss_orig = 0.001417, loss_refine = 0.000711 [Step 225] loss_orig = 0.001675, loss_refine = -1.868716 [Step 225] loss_orig = 0.001638, loss_refine = 0.002067 Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 497610: cache has only 0 modules 19%|█▊ | 226/1208 [3:12:06<14:32:21, 53.30s/it] {'loss': 0.0016, 'grad_norm': 5.2840298325236486, 'learning_rate': 8.129139072847682e-07, 'completion_length': 93.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.17817415793736777, 'kl': 0.0419921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 1.5} 19%|█▊ | 226/1208 [3:12:06<14:32:21, 53.30s/it]Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 498483: cache has only 0 modules Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 499356: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check out jony j's album'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 500229: cache has only 0 modules [Step 226] loss_orig = 0.000716, loss_refine = -1.499545[Step 226] loss_orig = 0.000906, loss_refine = -0.407870[Step 226] loss_orig = 0.001055, loss_refine = 0.682646 [Step 226] loss_orig = 0.001213, loss_refine = 0.683489 [Step 226] loss_orig = 0.000523, loss_refine = 0.683211[Step 226] loss_orig = 0.000863, loss_refine = 0.683023 [Step 226] loss_orig = 0.000678, loss_refine = -1.499069 [Step 226] loss_orig = 0.000956, loss_refine = 0.683969 19%|█▉ | 227/1208 [3:12:52<13:56:22, 51.15s/it] {'loss': 0.0012, 'grad_norm': 9.350894791606908, 'learning_rate': 8.120860927152317e-07, 'completion_length': 89.04166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3053751389185588, 'kl': 0.02655029296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 1.5} 19%|█▉ | 227/1208 [3:12:52<13:56:22, 51.15s/it]Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 501102: cache has only 0 modules Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 501975: cache has only 0 modules 19%|█▉ | 228/1208 [3:13:29<12:47:40, 47.00s/it] {'loss': 0.0008, 'grad_norm': 7.486963691323825, 'learning_rate': 8.112582781456954e-07, 'completion_length': 100.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 0.9375, 'reward': 2.4375, 'reward_std': 0.6392731368541718, 'kl': 0.0208740234375, 'clip_ratio': 0.0, 'epoch': 1.51} 19%|█▉ | 228/1208 [3:13:29<12:47:40, 47.00s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 502848: cache has only 0 modules Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 503721: cache has only 0 modules 19%|█▉ | 229/1208 [3:14:04<11:48:58, 43.45s/it] {'loss': 0.002, 'grad_norm': 5.1831373898001685, 'learning_rate': 8.104304635761589e-07, 'completion_length': 89.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0509033203125, 'clip_ratio': 0.0, 'epoch': 1.52} 19%|█▉ | 229/1208 [3:14:04<11:48:58, 43.45s/it]Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 504594: cache has only 0 modules Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 505467: cache has only 0 modules 19%|█▉ | 230/1208 [3:14:46<11:41:06, 43.01s/it] {'loss': 0.0018, 'grad_norm': 13.519472195949724, 'learning_rate': 8.096026490066224e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.045654296875, 'clip_ratio': 0.0, 'epoch': 1.52} 19%|█▉ | 230/1208 [3:14:46<11:41:06, 43.01s/it]Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 506340: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'choose watercolor brush style'. Your previous answer was [{'action': 'click', 'coordinate': [568, 2332]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 507213: cache has only 0 modules [Step 230] loss_orig = 0.000788, loss_refine = 0.354467 [Step 230] loss_orig = 0.000687, loss_refine = -2.472868[Step 230] loss_orig = 0.000587, loss_refine = 0.354787[Step 230] loss_orig = 0.001354, loss_refine = 0.357224 [Step 230] loss_orig = 0.001111, loss_refine = 0.359816 [Step 230] loss_orig = 0.000900, loss_refine = 0.355515 [Step 230] loss_orig = 0.004761, loss_refine = 0.354661 [Step 230] loss_orig = 0.000838, loss_refine = 0.354682 Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 508086: cache has only 0 modules 19%|█▉ | 231/1208 [3:15:41<12:38:09, 46.56s/it] {'loss': 0.002, 'grad_norm': 3.8557370333886665, 'learning_rate': 8.08774834437086e-07, 'completion_length': 96.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.0384521484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 1.53} 19%|█▉ | 231/1208 [3:15:41<12:38:09, 46.56s/it]Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 508959: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 509832: cache has only 0 modules 19%|█▉ | 232/1208 [3:16:20<12:01:56, 44.38s/it] {'loss': 0.0015, 'grad_norm': 5.652801257071093, 'learning_rate': 8.079470198675497e-07, 'completion_length': 95.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.44403792917728424, 'kl': 0.03759765625, 'clip_ratio': 0.0, 'epoch': 1.54} 19%|█▉ | 232/1208 [3:16:20<12:01:56, 44.38s/it]Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 510705: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 511578: cache has only 0 modules [Step 232] loss_orig = 0.000755, loss_refine = 0.935839[Step 232] loss_orig = 0.000486, loss_refine = -0.933694[Step 232] loss_orig = 0.000876, loss_refine = -0.932563 [Step 232] loss_orig = 0.000789, loss_refine = 0.936044 [Step 232] loss_orig = 0.000575, loss_refine = -0.933676[Step 232] loss_orig = 0.000578, loss_refine = 0.936656 [Step 232] loss_orig = 0.000695, loss_refine = 0.936873 [Step 232] loss_orig = 0.002159, loss_refine = -0.934286 Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 512451: cache has only 0 modules 19%|█▉ | 233/1208 [3:17:12<12:37:22, 46.61s/it] {'loss': 0.0013, 'grad_norm': 8.285393495808513, 'learning_rate': 8.071192052980133e-07, 'completion_length': 88.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.02642822265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.54} 19%|█▉ | 233/1208 [3:17:12<12:37:22, 46.61s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 513324: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 514197: cache has only 0 modules 19%|█▉ | 234/1208 [3:17:47<11:41:09, 43.19s/it] {'loss': 0.0015, 'grad_norm': 0.2753053285569301, 'learning_rate': 8.062913907284767e-07, 'completion_length': 90.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0386962890625, 'clip_ratio': 0.0, 'epoch': 1.55} 19%|█▉ | 234/1208 [3:17:47<11:41:09, 43.19s/it]Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 515070: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Line History View, group'. Your previous answer was [{'action': 'click', 'coordinate': [988, 259]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 515943: cache has only 0 modules [Step 234] loss_orig = -0.658766, loss_refine = 1.503213[Step 234] loss_orig = 0.668263, loss_refine = 0.645664[Step 234] loss_orig = -0.659738, loss_refine = -0.213483[Step 234] loss_orig = 1.988461, loss_refine = 0.644885 [Step 234] loss_orig = -0.657657, loss_refine = -1.071696[Step 234] loss_orig = 0.669347, loss_refine = 0.646346 [Step 234] loss_orig = -0.660188, loss_refine = -1.070763[Step 234] loss_orig = -0.657640, loss_refine = -1.072054 Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 516816: cache has only 0 modules 19%|█▉ | 235/1208 [3:18:46<12:55:00, 47.79s/it] {'loss': 0.0014, 'grad_norm': 12.094025677697731, 'learning_rate': 8.054635761589403e-07, 'completion_length': 95.45833333333333, 'rewards/accuracy_reward_action': 0.7916666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.2083333333333335, 'reward_std': 0.9938512444496155, 'kl': 0.06549072265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.56} 19%|█▉ | 235/1208 [3:18:46<12:55:00, 47.79s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 517689: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 518562: cache has only 0 modules 20%|█▉ | 236/1208 [3:19:19<11:44:12, 43.47s/it] {'loss': 0.0015, 'grad_norm': 6.340129363558592, 'learning_rate': 8.04635761589404e-07, 'completion_length': 85.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.03680419921875, 'clip_ratio': 0.0, 'epoch': 1.56} 20%|█▉ | 236/1208 [3:19:19<11:44:12, 43.47s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 519435: cache has only 0 modules Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 520308: cache has only 0 modules 20%|█▉ | 237/1208 [3:20:06<11:57:48, 44.35s/it] {'loss': 0.0016, 'grad_norm': 5.405635911241375, 'learning_rate': 8.038079470198675e-07, 'completion_length': 104.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0404052734375, 'clip_ratio': 0.0, 'epoch': 1.57} 20%|█▉ | 237/1208 [3:20:06<11:57:48, 44.35s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 521181: cache has only 0 modules Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 522054: cache has only 0 modules 20%|█▉ | 238/1208 [3:20:40<11:08:58, 41.38s/it] {'loss': 0.0011, 'grad_norm': 10.925526765462594, 'learning_rate': 8.029801324503311e-07, 'completion_length': 85.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.02777099609375, 'clip_ratio': 0.0, 'epoch': 1.58} 20%|█▉ | 238/1208 [3:20:40<11:08:58, 41.38s/it]Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 522927: cache has only 0 modules Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 523800: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 524673: cache has only 0 modules [Step 238] loss_orig = 0.001035, loss_refine = -1.618232 [Step 238] loss_orig = 0.001429, loss_refine = 0.542009 [Step 238] loss_orig = 0.001558, loss_refine = 0.542219 [Step 238] loss_orig = 0.001170, loss_refine = -1.618625 [Step 238] loss_orig = 0.001393, loss_refine = 0.556415 [Step 238] loss_orig = 0.001472, loss_refine = 0.540841 [Step 238] loss_orig = 0.001899, loss_refine = 0.540838 [Step 238] loss_orig = 0.001631, loss_refine = 0.541548 20%|█▉ | 239/1208 [3:21:50<13:26:34, 49.94s/it] {'loss': 0.0025, 'grad_norm': 16.329279277334038, 'learning_rate': 8.021523178807946e-07, 'completion_length': 95.16666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.39000560839970905, 'kl': 0.0396728515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.58} 20%|█▉ | 239/1208 [3:21:50<13:26:34, 49.94s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 525546: cache has only 0 modules Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 526419: cache has only 0 modules 20%|█▉ | 240/1208 [3:22:23<12:01:26, 44.72s/it] {'loss': 0.0025, 'grad_norm': 12.654655663410393, 'learning_rate': 8.013245033112583e-07, 'completion_length': 84.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.0635986328125, 'clip_ratio': 0.0, 'epoch': 1.59} 20%|█▉ | 240/1208 [3:22:23<12:01:26, 44.72s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 527292: cache has only 0 modules Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 528165: cache has only 0 modules 20%|█▉ | 241/1208 [3:23:02<11:33:57, 43.06s/it] {'loss': 0.0018, 'grad_norm': 6.940514776398198, 'learning_rate': 8.004966887417218e-07, 'completion_length': 96.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'epoch': 1.6} 20%|█▉ | 241/1208 [3:23:02<11:33:57, 43.06s/it]Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 529038: cache has only 0 modules Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 529911: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 530784: cache has only 0 modules [Step 241] loss_orig = 0.002930, loss_refine = 0.001840[Step 241] loss_orig = 0.001301, loss_refine = 0.003254[Step 241] loss_orig = 0.001964, loss_refine = 0.000917[Step 241] loss_orig = 0.002174, loss_refine = 0.002209 [Step 241] loss_orig = 0.001771, loss_refine = 0.002405 [Step 241] loss_orig = 0.002217, loss_refine = 0.003271 [Step 241] loss_orig = 0.002526, loss_refine = 0.001910 [Step 241] loss_orig = 0.001403, loss_refine = 0.001797 20%|██ | 242/1208 [3:23:58<12:38:04, 47.09s/it] {'loss': 0.0017, 'grad_norm': 12.086657584031247, 'learning_rate': 7.996688741721854e-07, 'completion_length': 96.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.04010009765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.6} 20%|██ | 242/1208 [3:23:58<12:38:04, 47.09s/it]Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 531657: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'exchange target and source city'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 532530: cache has only 0 modules [Step 242] loss_orig = 0.001571, loss_refine = -0.723712[Step 242] loss_orig = 0.001438, loss_refine = 1.208150[Step 242] loss_orig = 0.000372, loss_refine = 1.208862 [Step 242] loss_orig = 0.000561, loss_refine = 1.208571 [Step 242] loss_orig = 0.000443, loss_refine = -0.723317[Step 242] loss_orig = 0.001091, loss_refine = -0.723888[Step 242] loss_orig = 0.000963, loss_refine = -0.722046 [Step 242] loss_orig = 0.000673, loss_refine = -0.722085 Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 533403: cache has only 0 modules 20%|██ | 243/1208 [3:24:57<13:33:34, 50.58s/it] {'loss': 0.0021, 'grad_norm': 8.888280090005564, 'learning_rate': 7.988410596026491e-07, 'completion_length': 107.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.2903675138950348, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.61} 20%|██ | 243/1208 [3:24:57<13:33:34, 50.58s/it]Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 534276: cache has only 0 modules Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 535149: cache has only 0 modules 20%|██ | 244/1208 [3:25:33<12:21:05, 46.13s/it] {'loss': 0.0012, 'grad_norm': 4.167458142786579, 'learning_rate': 7.980132450331125e-07, 'completion_length': 96.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.029541015625, 'clip_ratio': 0.0, 'epoch': 1.62} 20%|██ | 244/1208 [3:25:33<12:21:05, 46.13s/it]Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 536022: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'add alarm to the included controls'. Your previous answer was [{'action': 'click', 'coordinate': [1143, 1552]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 536895: cache has only 0 modules [Step 244] loss_orig = 0.001135, loss_refine = 0.540431[Step 244] loss_orig = 0.000710, loss_refine = -1.618350 [Step 244] loss_orig = 0.001304, loss_refine = 0.540474 [Step 244] loss_orig = 0.000704, loss_refine = 0.542488 [Step 244] loss_orig = 0.000737, loss_refine = 0.541886 [Step 244] loss_orig = 0.001249, loss_refine = 0.541699 [Step 244] loss_orig = 0.001748, loss_refine = 0.541425 [Step 244] loss_orig = 0.000703, loss_refine = -1.619290 Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 537768: cache has only 0 modules ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 538641: cache has only 0 modules [Step 244] loss_orig = 0.003689, loss_refine = -0.348384 [Step 244] loss_orig = 0.003018, loss_refine = -0.351201[Step 244] loss_orig = 0.003102, loss_refine = -0.350513 [Step 244] loss_orig = 0.002034, loss_refine = -0.351523 [Step 244] loss_orig = 0.001484, loss_refine = -0.352040 [Step 244] loss_orig = 0.001977, loss_refine = -0.352001 [Step 244] loss_orig = 0.002105, loss_refine = -0.350271 [Step 244] loss_orig = 0.003639, loss_refine = 2.476583 20%|██ | 245/1208 [3:26:42<14:10:51, 53.01s/it] {'loss': 0.002, 'grad_norm': 6.464032140365083, 'learning_rate': 7.971854304635761e-07, 'completion_length': 94.28125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.96875, 'reward': 2.28125, 'reward_std': 0.2041158601641655, 'kl': 0.0458984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.62} 20%|██ | 245/1208 [3:26:42<14:10:51, 53.01s/it]Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 539514: cache has only 0 modules Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 540387: cache has only 0 modules 20%|██ | 246/1208 [3:27:24<13:18:46, 49.82s/it] {'loss': 0.001, 'grad_norm': 10.614554001213497, 'learning_rate': 7.963576158940397e-07, 'completion_length': 97.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.02520751953125, 'clip_ratio': 0.0, 'epoch': 1.63} 20%|██ | 246/1208 [3:27:24<13:18:46, 49.82s/it]Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 541260: cache has only 0 modules Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 542133: cache has only 0 modules 20%|██ | 247/1208 [3:28:06<12:39:41, 47.43s/it] {'loss': 0.0042, 'grad_norm': 6.560253471225623, 'learning_rate': 7.955298013245033e-07, 'completion_length': 99.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.1044921875, 'clip_ratio': 0.0, 'epoch': 1.64} 20%|██ | 247/1208 [3:28:06<12:39:41, 47.43s/it]Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 543006: cache has only 0 modules Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 543879: cache has only 0 modules 21%|██ | 248/1208 [3:28:55<12:48:49, 48.05s/it] {'loss': 0.0016, 'grad_norm': 6.218940210249614, 'learning_rate': 7.947019867549668e-07, 'completion_length': 104.4375, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.4432026147842407, 'kl': 0.0401611328125, 'clip_ratio': 0.0, 'epoch': 1.64} 21%|██ | 248/1208 [3:28:55<12:48:49, 48.05s/it]Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 544752: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open clock at 3'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 545625: cache has only 0 modules [Step 248] loss_orig = 0.000938, loss_refine = -0.353009[Step 248] loss_orig = 0.001387, loss_refine = -0.351928[Step 248] loss_orig = 0.001622, loss_refine = -0.352406 [Step 248] loss_orig = 0.001020, loss_refine = -0.352364[Step 248] loss_orig = 0.000749, loss_refine = -0.353088[Step 248] loss_orig = 0.002942, loss_refine = -0.350202 [Step 248] loss_orig = 0.001098, loss_refine = 2.475386 [Step 248] loss_orig = 0.001920, loss_refine = -0.352577 Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 546498: cache has only 0 modules 21%|██ | 249/1208 [3:29:57<13:53:40, 52.16s/it] {'loss': 0.0015, 'grad_norm': 6.990350881018853, 'learning_rate': 7.938741721854304e-07, 'completion_length': 95.95833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.125, 'reward_std': 0.47419944405555725, 'kl': 0.039306640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 1.65} 21%|██ | 249/1208 [3:29:57<13:53:40, 52.16s/it]Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 547371: cache has only 0 modules Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 548244: cache has only 0 modules 21%|██ | 250/1208 [3:30:36<12:49:36, 48.20s/it] {'loss': 0.0012, 'grad_norm': 15.260179807458254, 'learning_rate': 7.93046357615894e-07, 'completion_length': 99.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.030029296875, 'clip_ratio': 0.0, 'epoch': 1.66} 21%|██ | 250/1208 [3:30:36<12:49:36, 48.20s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 549117: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 549990: cache has only 0 modules 21%|██ | 251/1208 [3:31:21<12:31:03, 47.09s/it] {'loss': 0.0029, 'grad_norm': 21.554052554496128, 'learning_rate': 7.922185430463576e-07, 'completion_length': 96.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.636739045381546, 'kl': 0.07275390625, 'clip_ratio': 0.0, 'epoch': 1.66} 21%|██ | 251/1208 [3:31:21<12:31:03, 47.09s/it]Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 550863: cache has only 0 modules Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 551736: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'flod this content'. Your previous answer was [{'action': 'click', 'coordinate': [978, 343]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 552609: cache has only 0 modules [Step 251] loss_orig = 0.000558, loss_refine = -0.934141[Step 251] loss_orig = 0.000920, loss_refine = -0.931362 [Step 251] loss_orig = 0.001404, loss_refine = 0.935756 [Step 251] loss_orig = 0.001002, loss_refine = -0.934490 [Step 251] loss_orig = 0.000850, loss_refine = 0.936316[Step 251] loss_orig = 0.000737, loss_refine = -0.934069 [Step 251] loss_orig = 0.001045, loss_refine = 0.936307 [Step 251] loss_orig = 0.001841, loss_refine = 0.936035 21%|██ | 252/1208 [3:32:10<12:41:34, 47.80s/it] {'loss': 0.0011, 'grad_norm': 9.73592837954886, 'learning_rate': 7.913907284768212e-07, 'completion_length': 84.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.02471923828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.67} 21%|██ | 252/1208 [3:32:10<12:41:34, 47.80s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 553482: cache has only 0 modules Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 554355: cache has only 0 modules 21%|██ | 253/1208 [3:32:43<11:30:14, 43.37s/it] {'loss': 0.001, 'grad_norm': 0.18932533364599363, 'learning_rate': 7.905629139072847e-07, 'completion_length': 80.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02618408203125, 'clip_ratio': 0.0, 'epoch': 1.68} 21%|██ | 253/1208 [3:32:43<11:30:14, 43.37s/it]Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 555228: cache has only 0 modules Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 556101: cache has only 0 modules 21%|██ | 254/1208 [3:33:27<11:31:54, 43.52s/it] {'loss': 0.0014, 'grad_norm': 34.612218029041145, 'learning_rate': 7.897350993377483e-07, 'completion_length': 97.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.034423828125, 'clip_ratio': 0.0, 'epoch': 1.68} 21%|██ | 254/1208 [3:33:27<11:31:54, 43.52s/it]Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 556974: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 557847: cache has only 0 modules 21%|██ | 255/1208 [3:34:05<11:05:25, 41.89s/it] {'loss': 0.0017, 'grad_norm': 12.517307663127044, 'learning_rate': 7.889072847682119e-07, 'completion_length': 104.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 1.69} 21%|██ | 255/1208 [3:34:05<11:05:25, 41.89s/it]Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 558720: cache has only 0 modules Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 559593: cache has only 0 modules 21%|██ | 256/1208 [3:34:42<10:39:54, 40.33s/it] {'loss': 0.0011, 'grad_norm': 4.912267130619222, 'learning_rate': 7.880794701986755e-07, 'completion_length': 92.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02728271484375, 'clip_ratio': 0.0, 'epoch': 1.7} 21%|██ | 256/1208 [3:34:42<10:39:54, 40.33s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 560466: cache has only 0 modules Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 561339: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element deserts'. Your previous answer was [{'action': 'click', 'coordinate': [1054, 521]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 562212: cache has only 0 modules [Step 256] loss_orig = 0.001859, loss_refine = -1.133048 [Step 256] loss_orig = 0.003171, loss_refine = -0.123548 [Step 256] loss_orig = 0.002965, loss_refine = 0.885421 [Step 256] loss_orig = 0.001950, loss_refine = -1.133429 [Step 256] loss_orig = 0.001746, loss_refine = -1.133685 [Step 256] loss_orig = 0.002272, loss_refine = 0.885412[Step 256] loss_orig = 0.002145, loss_refine = 0.883383 [Step 256] loss_orig = 0.001727, loss_refine = 0.886642 21%|██▏ | 257/1208 [3:35:44<12:24:43, 46.99s/it] {'loss': 0.0027, 'grad_norm': 12.783820545859816, 'learning_rate': 7.87251655629139e-07, 'completion_length': 99.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.5028601288795471, 'kl': 0.068115234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.7} 21%|██▏ | 257/1208 [3:35:44<12:24:43, 46.99s/it]Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 563085: cache has only 0 modules Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 563958: cache has only 0 modules 21%|██▏ | 258/1208 [3:36:24<11:49:18, 44.80s/it] {'loss': 0.0011, 'grad_norm': 7.013585145089319, 'learning_rate': 7.864238410596026e-07, 'completion_length': 94.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0283203125, 'clip_ratio': 0.0, 'epoch': 1.71} 21%|██▏ | 258/1208 [3:36:24<11:49:18, 44.80s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 564831: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 565704: cache has only 0 modules 21%|██▏ | 259/1208 [3:36:58<10:55:27, 41.44s/it] {'loss': 0.0022, 'grad_norm': 5.706583233928073, 'learning_rate': 7.855960264900662e-07, 'completion_length': 83.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0550537109375, 'clip_ratio': 0.0, 'epoch': 1.72} 21%|██▏ | 259/1208 [3:36:58<10:55:27, 41.44s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 566577: cache has only 0 modules Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 567450: cache has only 0 modules 22%|██▏ | 260/1208 [3:37:35<10:36:44, 40.30s/it] {'loss': 0.0018, 'grad_norm': 9.624215778221714, 'learning_rate': 7.847682119205298e-07, 'completion_length': 91.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'epoch': 1.72} 22%|██▏ | 260/1208 [3:37:35<10:36:44, 40.30s/it]Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 568323: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functional icon'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 569196: cache has only 0 modules [Step 260] loss_orig = 0.000752, loss_refine = 0.540333[Step 260] loss_orig = 0.001296, loss_refine = 0.540466[Step 260] loss_orig = 0.001551, loss_refine = 0.540664 [Step 260] loss_orig = 0.001067, loss_refine = 0.540460[Step 260] loss_orig = 0.001072, loss_refine = 0.540829 [Step 260] loss_orig = 0.000989, loss_refine = 0.540934 [Step 260] loss_orig = 0.000692, loss_refine = -1.619387 [Step 260] loss_orig = 0.001703, loss_refine = -1.619035 Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 570069: cache has only 0 modules 22%|██▏ | 261/1208 [3:38:26<11:26:24, 43.49s/it] {'loss': 0.0012, 'grad_norm': 28.09227591393299, 'learning_rate': 7.839403973509933e-07, 'completion_length': 82.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.48112308979034424, 'kl': 0.0350341796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.73} 22%|██▏ | 261/1208 [3:38:26<11:26:24, 43.49s/it]Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 570942: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: RightScrollButton'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 571815: cache has only 0 modules [Step 261] loss_orig = 0.003859, loss_refine = 1.502117[Step 261] loss_orig = 0.003479, loss_refine = -0.680934[Step 261] loss_orig = 0.001715, loss_refine = 1.503504 [Step 261] loss_orig = 0.001392, loss_refine = -0.678746[Step 261] loss_orig = 0.001679, loss_refine = -0.681194 [Step 261] loss_orig = 0.000999, loss_refine = -0.680086[Step 261] loss_orig = 0.001048, loss_refine = -0.681297 [Step 261] loss_orig = 0.001967, loss_refine = 0.410796 Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 572688: cache has only 0 modules 22%|██▏ | 262/1208 [3:39:17<11:58:36, 45.58s/it] {'loss': 0.0019, 'grad_norm': 5.655190626167562, 'learning_rate': 7.831125827814569e-07, 'completion_length': 94.41666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5416666666666666, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.3053751389185588, 'kl': 0.0501708984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.74} 22%|██▏ | 262/1208 [3:39:17<11:58:36, 45.58s/it]Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 573561: cache has only 0 modules Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 574434: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - System'. Your previous answer was [{'action': 'click', 'coordinate': [2293, 22]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 575307: cache has only 0 modules [Step 262] loss_orig = 0.001529, loss_refine = 0.541798 [Step 262] loss_orig = 0.002640, loss_refine = 0.541553[Step 262] loss_orig = 0.004688, loss_refine = -1.617441 [Step 262] loss_orig = 0.001450, loss_refine = 0.541501 [Step 262] loss_orig = 0.003400, loss_refine = 0.541426[Step 262] loss_orig = 0.003334, loss_refine = -1.618927 [Step 262] loss_orig = 0.001378, loss_refine = 0.540903 [Step 262] loss_orig = 0.002837, loss_refine = 0.541151 22%|██▏ | 263/1208 [3:40:09<12:29:31, 47.59s/it] {'loss': 0.0038, 'grad_norm': 7.864406494067016, 'learning_rate': 7.822847682119205e-07, 'completion_length': 89.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.3268197377522786, 'kl': 0.10986328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.74} 22%|██▏ | 263/1208 [3:40:09<12:29:31, 47.59s/it]Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 576180: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Split screen'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 577053: cache has only 0 modules [Step 263] loss_orig = 0.004154, loss_refine = 0.355573 [Step 263] loss_orig = 0.002967, loss_refine = 0.355019 [Step 263] loss_orig = 0.002393, loss_refine = 0.355693[Step 263] loss_orig = 0.004277, loss_refine = 0.354769 [Step 263] loss_orig = 0.005876, loss_refine = 0.355005 [Step 263] loss_orig = 0.001949, loss_refine = 0.354466 [Step 263] loss_orig = 0.001942, loss_refine = 0.363457 [Step 263] loss_orig = 0.001485, loss_refine = -2.472829 Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 577926: cache has only 0 modules 22%|██▏ | 264/1208 [3:41:06<13:14:11, 50.48s/it] {'loss': 0.0022, 'grad_norm': 12.632058048377267, 'learning_rate': 7.81456953642384e-07, 'completion_length': 96.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.0616455078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.75} 22%|██▏ | 264/1208 [3:41:06<13:14:11, 50.48s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 578799: cache has only 0 modules Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 579672: cache has only 0 modules 22%|██▏ | 265/1208 [3:41:42<12:04:59, 46.13s/it] {'loss': 0.0015, 'grad_norm': 5.096031479269139, 'learning_rate': 7.806291390728477e-07, 'completion_length': 88.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0382080078125, 'clip_ratio': 0.0, 'epoch': 1.75} 22%|██▏ | 265/1208 [3:41:42<12:04:59, 46.13s/it]Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 580545: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Decorative Locked'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 581418: cache has only 0 modules [Step 265] loss_orig = 0.004566, loss_refine = -0.721266[Step 265] loss_orig = 0.001709, loss_refine = 1.209904 [Step 265] loss_orig = 0.001562, loss_refine = -0.722686 [Step 265] loss_orig = 0.002704, loss_refine = -0.719904 [Step 265] loss_orig = 0.002157, loss_refine = 1.208993 [Step 265] loss_orig = 0.002143, loss_refine = -0.722944 [Step 265] loss_orig = 0.002711, loss_refine = 1.208971 [Step 265] loss_orig = 0.001993, loss_refine = -0.723145 Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 582291: cache has only 0 modules 22%|██▏ | 266/1208 [3:42:46<13:29:41, 51.57s/it] {'loss': 0.003, 'grad_norm': 19.849314415442258, 'learning_rate': 7.798013245033113e-07, 'completion_length': 109.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.2903675138950348, 'kl': 0.076171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.76} 22%|██▏ | 266/1208 [3:42:46<13:29:41, 51.57s/it]Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 583164: cache has only 0 modules Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 584037: cache has only 0 modules 22%|██▏ | 267/1208 [3:43:20<12:06:27, 46.32s/it] {'loss': 0.0017, 'grad_norm': 1.3280152725536003, 'learning_rate': 7.789735099337747e-07, 'completion_length': 81.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04248046875, 'clip_ratio': 0.0, 'epoch': 1.77} 22%|██▏ | 267/1208 [3:43:20<12:06:27, 46.32s/it]Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 584910: cache has only 0 modules Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 585783: cache has only 0 modules 22%|██▏ | 268/1208 [3:43:59<11:29:49, 44.03s/it] {'loss': 0.0017, 'grad_norm': 22.30714376327514, 'learning_rate': 7.781456953642383e-07, 'completion_length': 101.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5345224738121033, 'kl': 0.042724609375, 'clip_ratio': 0.0, 'epoch': 1.77} 22%|██▏ | 268/1208 [3:43:59<11:29:49, 44.03s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 586656: cache has only 0 modules Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 587529: cache has only 0 modules 22%|██▏ | 269/1208 [3:44:31<10:31:50, 40.37s/it] {'loss': 0.0022, 'grad_norm': 7.494670254066604, 'learning_rate': 7.77317880794702e-07, 'completion_length': 80.75, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.8838834464550018, 'kl': 0.05419921875, 'clip_ratio': 0.0, 'epoch': 1.78} 22%|██▏ | 269/1208 [3:44:31<10:31:50, 40.37s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 588402: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 589275: cache has only 0 modules 22%|██▏ | 270/1208 [3:45:03<9:52:57, 37.93s/it] {'loss': 0.0056, 'grad_norm': 1.077155738683515, 'learning_rate': 7.764900662251656e-07, 'completion_length': 80.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.138916015625, 'clip_ratio': 0.0, 'epoch': 1.79} 22%|██▏ | 270/1208 [3:45:03<9:52:57, 37.93s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 590148: cache has only 0 modules Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 591021: cache has only 0 modules 22%|██▏ | 271/1208 [3:45:54<10:54:39, 41.92s/it] {'loss': 0.0024, 'grad_norm': 5.2857885056539615, 'learning_rate': 7.756622516556291e-07, 'completion_length': 101.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9375, 'reward': 2.125, 'reward_std': 0.6760360598564148, 'kl': 0.0595703125, 'clip_ratio': 0.0, 'epoch': 1.79} 22%|██▏ | 271/1208 [3:45:54<10:54:39, 41.92s/it]Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 591894: cache has only 0 modules Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 592767: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Font Name'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 593640: cache has only 0 modules [Step 271] loss_orig = 0.001251, loss_refine = 0.002101[Step 271] loss_orig = 0.001647, loss_refine = 0.001973[Step 271] loss_orig = 0.001617, loss_refine = 0.001280 [Step 271] loss_orig = 0.001442, loss_refine = 0.001887[Step 271] loss_orig = 0.001014, loss_refine = 0.001193 [Step 271] loss_orig = 0.001678, loss_refine = 0.002261 [Step 271] loss_orig = 0.002191, loss_refine = 0.001788 [Step 271] loss_orig = 0.002245, loss_refine = 0.001689 23%|██▎ | 272/1208 [3:46:42<11:21:10, 43.66s/it] {'loss': 0.0024, 'grad_norm': 5.733210991563676, 'learning_rate': 7.748344370860926e-07, 'completion_length': 88.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.11785112818082173, 'kl': 0.0589599609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 1.8} 23%|██▎ | 272/1208 [3:46:42<11:21:10, 43.66s/it]Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 594513: cache has only 0 modules Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 595386: cache has only 0 modules 23%|██▎ | 273/1208 [3:47:15<10:30:23, 40.45s/it] {'loss': 0.0026, 'grad_norm': 24.087999092169344, 'learning_rate': 7.740066225165563e-07, 'completion_length': 80.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.066162109375, 'clip_ratio': 0.0, 'epoch': 1.81} 23%|██▎ | 273/1208 [3:47:15<10:30:23, 40.45s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 596259: cache has only 0 modules Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 597132: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'invert the lens'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 598005: cache has only 0 modules [Step 273] loss_orig = 0.001774, loss_refine = 0.001588 [Step 273] loss_orig = 0.001880, loss_refine = 0.004381 [Step 273] loss_orig = 0.001462, loss_refine = 0.001142 [Step 273] loss_orig = 0.001665, loss_refine = 0.000824[Step 273] loss_orig = 0.002448, loss_refine = 0.001272 [Step 273] loss_orig = 0.001046, loss_refine = 0.001563 [Step 273] loss_orig = 0.005963, loss_refine = 0.001755 [Step 273] loss_orig = 0.001220, loss_refine = 0.002247 23%|██▎ | 274/1208 [3:48:06<11:18:29, 43.59s/it] {'loss': 0.0018, 'grad_norm': 0.514783395540369, 'learning_rate': 7.731788079470198e-07, 'completion_length': 84.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.0489501953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.81} 23%|██▎ | 274/1208 [3:48:06<11:18:29, 43.59s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 598878: cache has only 0 modules Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 599751: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Zoom 376%'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 600624: cache has only 0 modules [Step 274] loss_orig = 0.001708, loss_refine = -0.189798[Step 274] loss_orig = 0.001493, loss_refine = -0.191479 [Step 274] loss_orig = 0.006994, loss_refine = -0.190695[Step 274] loss_orig = 0.001850, loss_refine = -0.193497 [Step 274] loss_orig = 0.002716, loss_refine = -0.191720[Step 274] loss_orig = 0.002714, loss_refine = 1.366513 [Step 274] loss_orig = 0.001765, loss_refine = -1.747558 [Step 274] loss_orig = 0.002318, loss_refine = 1.366432 23%|██▎ | 275/1208 [3:49:00<12:03:53, 46.55s/it] {'loss': 0.0032, 'grad_norm': 10.195380195113787, 'learning_rate': 7.723509933774834e-07, 'completion_length': 92.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3679266770680745, 'kl': 0.068359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.82} 23%|██▎ | 275/1208 [3:49:00<12:03:53, 46.55s/it]Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 601497: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 602370: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [600, 1416]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 603243: cache has only 0 modules [Step 275] loss_orig = 0.001743, loss_refine = 0.003005 [Step 275] loss_orig = 0.002383, loss_refine = -1.078448[Step 275] loss_orig = 0.002314, loss_refine = 0.001419 [Step 275] loss_orig = 0.002458, loss_refine = 0.001409 [Step 275] loss_orig = 0.001800, loss_refine = -1.078176 [Step 275] loss_orig = 0.003810, loss_refine = 0.001886 [Step 275] loss_orig = 0.002623, loss_refine = 0.001565 [Step 275] loss_orig = 0.006070, loss_refine = 2.160830 23%|██▎ | 276/1208 [3:50:21<14:44:44, 56.96s/it] {'loss': 0.0016, 'grad_norm': 5.536478916890325, 'learning_rate': 7.715231788079471e-07, 'completion_length': 104.0, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.125, 'reward_std': 0.6621600786844889, 'kl': 0.0565185546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.83} 23%|██▎ | 276/1208 [3:50:21<14:44:44, 56.96s/it]Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 604116: cache has only 0 modules Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 604989: cache has only 0 modules 23%|██▎ | 277/1208 [3:50:51<12:41:36, 49.08s/it] {'loss': 0.0015, 'grad_norm': 6.972767600503796, 'learning_rate': 7.706953642384106e-07, 'completion_length': 79.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.036376953125, 'clip_ratio': 0.0, 'epoch': 1.83} 23%|██▎ | 277/1208 [3:50:51<12:41:36, 49.08s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 605862: cache has only 0 modules Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 606735: cache has only 0 modules 23%|██▎ | 278/1208 [3:51:31<11:55:48, 46.18s/it] {'loss': 0.002, 'grad_norm': 10.23268136167967, 'learning_rate': 7.698675496688741e-07, 'completion_length': 89.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.0501708984375, 'clip_ratio': 0.0, 'epoch': 1.84} 23%|██▎ | 278/1208 [3:51:31<11:55:48, 46.18s/it]Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 607608: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 608481: cache has only 0 modules 23%|██▎ | 279/1208 [3:52:09<11:18:35, 43.83s/it] {'loss': 0.0025, 'grad_norm': 8.428395713585733, 'learning_rate': 7.690397350993377e-07, 'completion_length': 87.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0625, 'clip_ratio': 0.0, 'epoch': 1.85} 23%|██▎ | 279/1208 [3:52:09<11:18:35, 43.83s/it]Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 609354: cache has only 0 modules Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 610227: cache has only 0 modules 23%|██▎ | 280/1208 [3:52:45<10:38:48, 41.30s/it] {'loss': 0.0033, 'grad_norm': 10.018781733896526, 'learning_rate': 7.682119205298014e-07, 'completion_length': 76.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.08154296875, 'clip_ratio': 0.0, 'epoch': 1.85} 23%|██▎ | 280/1208 [3:52:45<10:38:48, 41.30s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 611100: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 611973: cache has only 0 modules 23%|██▎ | 281/1208 [3:53:13<9:40:29, 37.57s/it] {'loss': 0.0026, 'grad_norm': 4.530145102269853, 'learning_rate': 7.673841059602648e-07, 'completion_length': 74.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0650634765625, 'clip_ratio': 0.0, 'epoch': 1.86} 23%|██▎ | 281/1208 [3:53:13<9:40:29, 37.57s/it]Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 612846: cache has only 0 modules Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 613719: cache has only 0 modules 23%|██▎ | 282/1208 [3:53:58<10:13:00, 39.72s/it] {'loss': 0.0018, 'grad_norm': 7.553652738508583, 'learning_rate': 7.665562913907284e-07, 'completion_length': 91.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.046142578125, 'clip_ratio': 0.0, 'epoch': 1.87} 23%|██▎ | 282/1208 [3:53:58<10:13:00, 39.72s/it]Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 614592: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'manage the outlayer'. Your previous answer was [{'action': 'click', 'coordinate': [869, 364]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 615465: cache has only 0 modules [Step 282] loss_orig = 2.476664, loss_refine = -1.408075[Step 282] loss_orig = -0.351157, loss_refine = 0.847739[Step 282] loss_orig = -0.350094, loss_refine = 0.847564 [Step 282] loss_orig = -0.350185, loss_refine = -1.408375 [Step 282] loss_orig = -0.351494, loss_refine = 0.847063 [Step 282] loss_orig = -0.351336, loss_refine = -0.280717 [Step 282] loss_orig = -0.351141, loss_refine = 0.847178 [Step 282] loss_orig = -0.351241, loss_refine = -0.277685 Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 616338: cache has only 0 modules 23%|██▎ | 283/1208 [3:54:49<11:02:37, 42.98s/it] {'loss': 0.0022, 'grad_norm': 26.189548586325763, 'learning_rate': 7.65728476821192e-07, 'completion_length': 82.54166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.531170666217804, 'kl': 0.064453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.87} 23%|██▎ | 283/1208 [3:54:49<11:02:37, 42.98s/it]Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 617211: cache has only 0 modules Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 618084: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [1011, 966]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 618957: cache has only 0 modules [Step 283] loss_orig = 0.000802, loss_refine = 0.541127[Step 283] loss_orig = 0.001651, loss_refine = -1.618730 [Step 283] loss_orig = 0.001509, loss_refine = 0.540592 [Step 283] loss_orig = 0.000945, loss_refine = 0.540765 [Step 283] loss_orig = 0.001347, loss_refine = 0.540570 [Step 283] loss_orig = 0.001793, loss_refine = 0.540767 [Step 283] loss_orig = 0.000954, loss_refine = -1.618956 [Step 283] loss_orig = 0.000802, loss_refine = 0.541000 24%|██▎ | 284/1208 [3:55:37<11:25:34, 44.52s/it] {'loss': 0.0014, 'grad_norm': 4.138406938882526, 'learning_rate': 7.649006622516557e-07, 'completion_length': 71.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.03948974609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 1.88} 24%|██▎ | 284/1208 [3:55:37<11:25:34, 44.52s/it]Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 619830: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 620703: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 621576: cache has only 0 modules [Step 284] loss_orig = 0.001179, loss_refine = 0.726898 [Step 284] loss_orig = 0.000823, loss_refine = -1.206098[Step 284] loss_orig = 0.002088, loss_refine = 0.726310 [Step 284] loss_orig = 0.002662, loss_refine = -1.205243 [Step 284] loss_orig = 0.001353, loss_refine = 0.725582 [Step 284] loss_orig = 0.004276, loss_refine = -1.206576 [Step 284] loss_orig = 0.003583, loss_refine = 0.725426 [Step 284] loss_orig = 0.000798, loss_refine = 0.725634 24%|██▎ | 285/1208 [3:56:42<12:58:29, 50.61s/it] {'loss': 0.0017, 'grad_norm': 32.972780698458195, 'learning_rate': 7.640728476821192e-07, 'completion_length': 93.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.3450327714284261, 'kl': 0.0511474609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.89} 24%|██▎ | 285/1208 [3:56:42<12:58:29, 50.61s/it]Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 622449: cache has only 0 modules Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 623322: cache has only 0 modules 24%|██▎ | 286/1208 [3:57:18<11:53:53, 46.46s/it] {'loss': 0.003, 'grad_norm': 30.727594153698, 'learning_rate': 7.632450331125827e-07, 'completion_length': 90.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.0738525390625, 'clip_ratio': 0.0, 'epoch': 1.89} 24%|██▎ | 286/1208 [3:57:19<11:53:53, 46.46s/it]Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 624195: cache has only 0 modules Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 625068: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Zoom out'. Your previous answer was [{'action': 'click', 'coordinate': [1297, 115]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 625941: cache has only 0 modules [Step 286] loss_orig = 0.002573, loss_refine = 0.726174[Step 286] loss_orig = 0.002091, loss_refine = -1.204659[Step 286] loss_orig = 0.002445, loss_refine = -1.205616 [Step 286] loss_orig = 0.002344, loss_refine = 0.726604 [Step 286] loss_orig = 0.001546, loss_refine = 0.726724[Step 286] loss_orig = 0.002505, loss_refine = 0.726910 [Step 286] loss_orig = 0.001972, loss_refine = 0.726429 [Step 286] loss_orig = 0.003500, loss_refine = -1.204701 24%|██▍ | 287/1208 [3:58:10<12:18:40, 48.12s/it] {'loss': 0.0023, 'grad_norm': 11.993793988464919, 'learning_rate': 7.624172185430463e-07, 'completion_length': 79.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.17251638571421304, 'kl': 0.058837890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 1.9} 24%|██▍ | 287/1208 [3:58:11<12:18:40, 48.12s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 626814: cache has only 0 modules Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 627687: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 628560: cache has only 0 modules [Step 287] loss_orig = 0.002625, loss_refine = 0.941511[Step 287] loss_orig = 0.001811, loss_refine = -0.933775 [Step 287] loss_orig = 0.001405, loss_refine = -0.923618 [Step 287] loss_orig = 0.002194, loss_refine = -0.933392[Step 287] loss_orig = 0.003175, loss_refine = -0.933345 [Step 287] loss_orig = 0.002163, loss_refine = 0.945968 [Step 287] loss_orig = 0.001896, loss_refine = 0.937585 [Step 287] loss_orig = 0.001883, loss_refine = 0.936489 24%|██▍ | 288/1208 [3:59:02<12:32:18, 49.06s/it] {'loss': 0.0032, 'grad_norm': 5.9642738066178005, 'learning_rate': 7.615894039735099e-07, 'completion_length': 84.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.047607421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 1.91} 24%|██▍ | 288/1208 [3:59:02<12:32:18, 49.06s/it]Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 629433: cache has only 0 modules Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 630306: cache has only 0 modules 24%|██▍ | 289/1208 [3:59:35<11:20:35, 44.43s/it] {'loss': 0.0017, 'grad_norm': 8.740937869816836, 'learning_rate': 7.607615894039735e-07, 'completion_length': 79.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.0413818359375, 'clip_ratio': 0.0, 'epoch': 1.91} 24%|██▍ | 289/1208 [3:59:35<11:20:35, 44.43s/it]Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 631179: cache has only 0 modules Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 632052: cache has only 0 modules 24%|██▍ | 290/1208 [4:00:22<11:29:17, 45.05s/it] {'loss': 0.0049, 'grad_norm': 9.969117205173301, 'learning_rate': 7.599337748344371e-07, 'completion_length': 84.5625, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.6722923070192337, 'kl': 0.122314453125, 'clip_ratio': 0.0, 'epoch': 1.92} 24%|██▍ | 290/1208 [4:00:22<11:29:17, 45.05s/it]Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 632925: cache has only 0 modules Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 633798: cache has only 0 modules 24%|██▍ | 291/1208 [4:01:02<11:03:45, 43.43s/it] {'loss': 0.0019, 'grad_norm': 6.812565236805016, 'learning_rate': 7.591059602649006e-07, 'completion_length': 98.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'epoch': 1.93} 24%|██▍ | 291/1208 [4:01:02<11:03:45, 43.43s/it]Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 634671: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 635544: cache has only 0 modules [Step 291] loss_orig = 0.004018, loss_refine = -0.351338[Step 291] loss_orig = 0.003494, loss_refine = -0.351320 [Step 291] loss_orig = 0.001437, loss_refine = -0.351615 [Step 291] loss_orig = 0.002027, loss_refine = -0.351350[Step 291] loss_orig = 0.004445, loss_refine = 2.476406 [Step 291] loss_orig = 0.004636, loss_refine = -0.350690 [Step 291] loss_orig = 0.004389, loss_refine = -0.351682 [Step 291] loss_orig = 0.002198, loss_refine = -0.351665 Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 636417: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open dynamic shot'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 637290: cache has only 0 modules [Step 291] loss_orig = 0.001999, loss_refine = 0.546015[Step 291] loss_orig = 0.000683, loss_refine = 0.541251[Step 291] loss_orig = 0.001790, loss_refine = 0.546287[Step 291] loss_orig = 0.001346, loss_refine = 0.544803 [Step 291] loss_orig = 0.002116, loss_refine = -1.618882 [Step 291] loss_orig = 0.002499, loss_refine = 0.542196 [Step 291] loss_orig = 0.001366, loss_refine = 0.541251 [Step 291] loss_orig = 0.003795, loss_refine = -1.617495 24%|██▍ | 292/1208 [4:02:09<12:51:37, 50.54s/it] {'loss': 0.0026, 'grad_norm': 25.467468755270147, 'learning_rate': 7.582781456953642e-07, 'completion_length': 95.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.28125, 'reward_std': 0.2041158601641655, 'kl': 0.06591796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5625, 'epoch': 1.93} 24%|██▍ | 292/1208 [4:02:09<12:51:37, 50.54s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 638163: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 639036: cache has only 0 modules 24%|██▍ | 293/1208 [4:02:42<11:33:37, 45.48s/it] {'loss': 0.002, 'grad_norm': 0.35346800398351663, 'learning_rate': 7.574503311258278e-07, 'completion_length': 79.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0509033203125, 'clip_ratio': 0.0, 'epoch': 1.94} 24%|██▍ | 293/1208 [4:02:42<11:33:37, 45.48s/it]Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 639909: cache has only 0 modules Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 640782: cache has only 0 modules 24%|██▍ | 294/1208 [4:03:13<10:24:35, 41.00s/it] {'loss': 0.0022, 'grad_norm': 4.135535475015043, 'learning_rate': 7.566225165562914e-07, 'completion_length': 71.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0560302734375, 'clip_ratio': 0.0, 'epoch': 1.95} 24%|██▍ | 294/1208 [4:03:13<10:24:35, 41.00s/it]Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 641655: cache has only 0 modules Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 642528: cache has only 0 modules 24%|██▍ | 295/1208 [4:03:49<10:00:57, 39.49s/it] {'loss': 0.0022, 'grad_norm': 6.1115401700498, 'learning_rate': 7.557947019867549e-07, 'completion_length': 81.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.5303300768136978, 'kl': 0.054443359375, 'clip_ratio': 0.0, 'epoch': 1.95} 24%|██▍ | 295/1208 [4:03:49<10:00:57, 39.49s/it]Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 643401: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 644274: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 645147: cache has only 0 modules [Step 295] loss_orig = 0.001645, loss_refine = -0.538405[Step 295] loss_orig = 0.002923, loss_refine = -0.537847[Step 295] loss_orig = 0.001863, loss_refine = -0.536937[Step 295] loss_orig = 0.001739, loss_refine = -0.537013 [Step 295] loss_orig = 0.002608, loss_refine = -0.537552[Step 295] loss_orig = 0.002927, loss_refine = -0.538533[Step 295] loss_orig = 0.001406, loss_refine = 1.622243 [Step 295] loss_orig = 0.001674, loss_refine = 1.621928 25%|██▍ | 296/1208 [4:04:41<10:59:48, 43.41s/it] {'loss': 0.0017, 'grad_norm': 13.931504508105856, 'learning_rate': 7.549668874172185e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.30860670407613117, 'kl': 0.04095458984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 1.96} 25%|██▍ | 296/1208 [4:04:41<10:59:48, 43.41s/it]Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 646020: cache has only 0 modules Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 646893: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'adjust the voice'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 647766: cache has only 0 modules [Step 296] loss_orig = 0.003062, loss_refine = -1.617769[Step 296] loss_orig = 0.003598, loss_refine = -1.616877 [Step 296] loss_orig = 0.001946, loss_refine = 0.546225 [Step 296] loss_orig = 0.002565, loss_refine = 0.541638[Step 296] loss_orig = 0.004502, loss_refine = 0.540636[Step 296] loss_orig = 0.003264, loss_refine = 0.542068 [Step 296] loss_orig = 0.003197, loss_refine = 0.541599 [Step 296] loss_orig = 0.002309, loss_refine = 0.544287 25%|██▍ | 297/1208 [4:05:28<11:11:58, 44.26s/it] {'loss': 0.0021, 'grad_norm': 6.52886954190173, 'learning_rate': 7.541390728476821e-07, 'completion_length': 78.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.15430335203806558, 'kl': 0.05615234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 1.97} 25%|██▍ | 297/1208 [4:05:28<11:11:58, 44.26s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 648639: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 649512: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: topic-link-a151002'. Your previous answer was [{'action': 'click', 'coordinate': [1571, 532]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 650385: cache has only 0 modules [Step 297] loss_orig = 0.002012, loss_refine = 0.356575 [Step 297] loss_orig = 0.001462, loss_refine = 0.355398[Step 297] loss_orig = 0.001493, loss_refine = 0.354664 [Step 297] loss_orig = 0.000980, loss_refine = -2.472090 [Step 297] loss_orig = 0.003769, loss_refine = 0.355343 [Step 297] loss_orig = 0.001302, loss_refine = 0.354945 [Step 297] loss_orig = 0.002412, loss_refine = 0.354672[Step 297] loss_orig = 0.002270, loss_refine = 0.355511 25%|██▍ | 298/1208 [4:06:23<11:59:30, 47.44s/it] {'loss': 0.0025, 'grad_norm': 17.67617063929495, 'learning_rate': 7.533112582781456e-07, 'completion_length': 87.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.0648193359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 1.97} 25%|██▍ | 298/1208 [4:06:23<11:59:30, 47.44s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 651258: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 652131: cache has only 0 modules 25%|██▍ | 299/1208 [4:06:57<10:58:15, 43.45s/it] {'loss': 0.0026, 'grad_norm': 4.770343919682435, 'learning_rate': 7.524834437086093e-07, 'completion_length': 88.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.064697265625, 'clip_ratio': 0.0, 'epoch': 1.98} 25%|██▍ | 299/1208 [4:06:57<10:58:15, 43.45s/it]Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 653004: cache has only 0 modules Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 653877: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 654750: cache has only 0 modules [Step 299] loss_orig = 0.001858, loss_refine = 1.062084[Step 299] loss_orig = 0.001092, loss_refine = 1.063209[Step 299] loss_orig = 0.001095, loss_refine = -0.352013 [Step 299] loss_orig = 0.000651, loss_refine = -0.351938 [Step 299] loss_orig = 0.001254, loss_refine = 1.061815 [Step 299] loss_orig = 0.001421, loss_refine = -1.765965 [Step 299] loss_orig = 0.001642, loss_refine = -0.352608 [Step 299] loss_orig = 0.001431, loss_refine = -0.352286 25%|██▍ | 300/1208 [4:07:48<11:34:28, 45.89s/it] {'loss': 0.0016, 'grad_norm': 5.5022433253799115, 'learning_rate': 7.516556291390728e-07, 'completion_length': 88.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.0367431640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 1.99} 25%|██▍ | 300/1208 [4:07:48<11:34:28, 45.89s/it]Start loss calc for inst: click the UI element Gente TMRG /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 655623: cache has only 0 modules Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 656496: cache has only 0 modules 25%|██▍ | 301/1208 [4:08:41<12:06:57, 48.09s/it] {'loss': 0.0025, 'grad_norm': 5.063780679464805, 'learning_rate': 7.508278145695363e-07, 'completion_length': 82.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.06298828125, 'clip_ratio': 0.0, 'epoch': 1.99} 25%|██▍ | 301/1208 [4:08:41<12:06:57, 48.09s/it]Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 0.8333333730697632 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 657369: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 658242: cache has only 0 modules [Step 301] loss_orig = -0.352123, loss_refine = -0.350216[Step 301] loss_orig = -0.351500, loss_refine = -0.351261 [Step 301] loss_orig = -0.351161, loss_refine = -0.351350 [Step 301] loss_orig = -0.352024, loss_refine = -0.350170[Step 301] loss_orig = -0.351717, loss_refine = -0.351766 [Step 301] loss_orig = 2.475699, loss_refine = -0.352063 [Step 301] loss_orig = -0.351624, loss_refine = -0.351804 [Step 301] loss_orig = -0.351724, loss_refine = 2.475940 Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 0.8333333730697632 Reward function name: accuracy_reward_coord Reward: 0.8333333730697632 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 659115: cache has only 0 modules 25%|██▌ | 302/1208 [4:09:44<13:12:09, 52.46s/it] {'loss': 0.0034, 'grad_norm': 6.968045042191679, 'learning_rate': 7.5e-07, 'completion_length': 78.16667175292969, 'rewards/accuracy_reward_action': 0.8888889153798422, 'rewards/accuracy_reward_coord': 0.2777777910232544, 'rewards/format_reward': 1.0, 'reward': 2.5000000397364297, 'reward_std': 0.707106759150823, 'kl': 0.0899658203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.0} 25%|██▌ | 302/1208 [4:09:44<13:12:09, 52.46s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 659988: cache has only 0 modules Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 660861: cache has only 0 modules 25%|██▌ | 303/1208 [4:10:20<11:55:32, 47.44s/it] {'loss': 0.002, 'grad_norm': 0.28296019898275765, 'learning_rate': 7.491721854304636e-07, 'completion_length': 78.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.050537109375, 'clip_ratio': 0.0, 'epoch': 2.01} 25%|██▌ | 303/1208 [4:10:20<11:55:32, 47.44s/it]Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 661734: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 662607: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Show translate options'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 663480: cache has only 0 modules [Step 303] loss_orig = -0.351408, loss_refine = 0.941937[Step 303] loss_orig = -0.351589, loss_refine = 0.937294 [Step 303] loss_orig = -0.351869, loss_refine = -0.928295 [Step 303] loss_orig = -0.352054, loss_refine = 0.936695[Step 303] loss_orig = -0.351540, loss_refine = -0.932679 [Step 303] loss_orig = -0.351122, loss_refine = -0.933048[Step 303] loss_orig = -0.351202, loss_refine = -0.933305 [Step 303] loss_orig = 2.491942, loss_refine = 0.937414 25%|██▌ | 304/1208 [4:11:14<12:27:06, 49.59s/it] {'loss': 0.0032, 'grad_norm': 26.221047350818655, 'learning_rate': 7.483443708609272e-07, 'completion_length': 90.91666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.46854167183240253, 'kl': 0.088623046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.01} 25%|██▌ | 304/1208 [4:11:14<12:27:06, 49.59s/it]Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 664353: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 665226: cache has only 0 modules 25%|██▌ | 305/1208 [4:11:44<10:54:00, 43.46s/it] {'loss': 0.0018, 'grad_norm': 5.220302101747224, 'learning_rate': 7.475165562913906e-07, 'completion_length': 76.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0458984375, 'clip_ratio': 0.0, 'epoch': 2.02} 25%|██▌ | 305/1208 [4:11:44<10:54:00, 43.46s/it]Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 666099: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open dynamic shot'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 666972: cache has only 0 modules [Step 305] loss_orig = 0.002829, loss_refine = 0.841146[Step 305] loss_orig = 0.003859, loss_refine = -1.845662 [Step 305] loss_orig = 0.001008, loss_refine = 0.841874 [Step 305] loss_orig = 0.002288, loss_refine = -0.502708 [Step 305] loss_orig = 0.002208, loss_refine = 0.841384[Step 305] loss_orig = 0.001268, loss_refine = -0.500312 [Step 305] loss_orig = 0.002481, loss_refine = 0.841245 [Step 305] loss_orig = 0.002563, loss_refine = -0.502816 Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 667845: cache has only 0 modules 25%|██▌ | 306/1208 [4:12:33<11:19:07, 45.17s/it] {'loss': 0.0018, 'grad_norm': 10.777763324394268, 'learning_rate': 7.466887417218543e-07, 'completion_length': 90.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.3658590614795685, 'kl': 0.051513671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.03} 25%|██▌ | 306/1208 [4:12:33<11:19:07, 45.17s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 668718: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 669591: cache has only 0 modules 25%|██▌ | 307/1208 [4:13:09<10:39:58, 42.62s/it] {'loss': 0.0028, 'grad_norm': 15.672422485555577, 'learning_rate': 7.458609271523179e-07, 'completion_length': 83.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0709228515625, 'clip_ratio': 0.0, 'epoch': 2.03} 25%|██▌ | 307/1208 [4:13:09<10:39:58, 42.62s/it]Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 670464: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Intense Emphasis'. Your previous answer was [{'action': 'click', 'coordinate': [1600, 116]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 671337: cache has only 0 modules [Step 307] loss_orig = 0.002354, loss_refine = -0.193189 [Step 307] loss_orig = 0.002719, loss_refine = -0.191585 [Step 307] loss_orig = 0.002486, loss_refine = -0.193812[Step 307] loss_orig = 0.001666, loss_refine = -1.752513[Step 307] loss_orig = 0.003166, loss_refine = 1.366771 [Step 307] loss_orig = 0.001701, loss_refine = 1.366534 [Step 307] loss_orig = 0.002332, loss_refine = -0.190568 [Step 307] loss_orig = 0.003778, loss_refine = -0.193671 Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 672210: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. Your previous answer was [{'action': 'click', 'coordinate': [577, 101]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 673083: cache has only 0 modules [Step 307] loss_orig = 0.001672, loss_refine = 0.001157 [Step 307] loss_orig = 0.001824, loss_refine = 0.001752 [Step 307] loss_orig = 0.001575, loss_refine = 0.001824[Step 307] loss_orig = 0.002287, loss_refine = 0.000991 [Step 307] loss_orig = 0.002448, loss_refine = -1.869681[Step 307] loss_orig = 0.003135, loss_refine = 0.001723 [Step 307] loss_orig = 0.001742, loss_refine = 0.002438 [Step 307] loss_orig = 0.002343, loss_refine = 1.872410 25%|██▌ | 308/1208 [4:14:25<13:06:59, 52.47s/it] {'loss': 0.0019, 'grad_norm': 14.48452574535396, 'learning_rate': 7.450331125827814e-07, 'completion_length': 96.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.96875, 'reward': 2.21875, 'reward_std': 0.29384811222553253, 'kl': 0.05810546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 2.04} 25%|██▌ | 308/1208 [4:14:25<13:06:59, 52.47s/it]Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 673956: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 674829: cache has only 0 modules 26%|██▌ | 309/1208 [4:15:01<11:53:10, 47.60s/it] {'loss': 0.0024, 'grad_norm': 5.294917157759723, 'learning_rate': 7.44205298013245e-07, 'completion_length': 87.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0611572265625, 'clip_ratio': 0.0, 'epoch': 2.05} 26%|██▌ | 309/1208 [4:15:01<11:53:10, 47.60s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 675702: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 676575: cache has only 0 modules 26%|██▌ | 310/1208 [4:15:34<10:44:09, 43.04s/it] {'loss': 0.004, 'grad_norm': 6.284089494908739, 'learning_rate': 7.433774834437086e-07, 'completion_length': 77.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.1005859375, 'clip_ratio': 0.0, 'epoch': 2.05} 26%|██▌ | 310/1208 [4:15:34<10:44:09, 43.04s/it]Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 677448: cache has only 0 modules Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 678321: cache has only 0 modules 26%|██▌ | 311/1208 [4:16:05<9:52:30, 39.63s/it] {'loss': 0.0027, 'grad_norm': 13.269471131052308, 'learning_rate': 7.425496688741721e-07, 'completion_length': 85.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.068603515625, 'clip_ratio': 0.0, 'epoch': 2.06} 26%|██▌ | 311/1208 [4:16:05<9:52:30, 39.63s/it]Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 679194: cache has only 0 modules Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 680067: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 680940: cache has only 0 modules [Step 311] loss_orig = 0.001258, loss_refine = 0.545749[Step 311] loss_orig = 0.002609, loss_refine = -1.618679[Step 311] loss_orig = 0.002316, loss_refine = 0.541499 [Step 311] loss_orig = 0.000974, loss_refine = 0.542487[Step 311] loss_orig = 0.001258, loss_refine = -1.617615 [Step 311] loss_orig = 0.002094, loss_refine = 0.540821 [Step 311] loss_orig = 0.001064, loss_refine = 0.541375 [Step 311] loss_orig = 0.001331, loss_refine = 0.541584 26%|██▌ | 312/1208 [4:16:54<10:34:41, 42.50s/it] {'loss': 0.0018, 'grad_norm': 3.8005893987125825, 'learning_rate': 7.417218543046357e-07, 'completion_length': 82.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.039306640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.07} 26%|██▌ | 312/1208 [4:16:54<10:34:41, 42.50s/it]Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 681813: cache has only 0 modules Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 682686: cache has only 0 modules 26%|██▌ | 313/1208 [4:17:30<10:04:47, 40.54s/it] {'loss': 0.0018, 'grad_norm': 8.504791153371503, 'learning_rate': 7.408940397350993e-07, 'completion_length': 93.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5175491571426392, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'epoch': 2.07} 26%|██▌ | 313/1208 [4:17:30<10:04:47, 40.54s/it]Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 683559: cache has only 0 modules ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element System'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 684432: cache has only 0 modules [Step 313] loss_orig = 0.001856, loss_refine = 0.002211[Step 313] loss_orig = 0.001330, loss_refine = 0.002027[Step 313] loss_orig = 0.002138, loss_refine = 0.002088 [Step 313] loss_orig = 0.002870, loss_refine = 0.001692 [Step 313] loss_orig = 0.004338, loss_refine = 0.004074[Step 313] loss_orig = 0.001630, loss_refine = 0.003038 [Step 313] loss_orig = 0.002072, loss_refine = 0.002321 [Step 313] loss_orig = 0.002545, loss_refine = 0.002044 Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 685305: cache has only 0 modules 26%|██▌ | 314/1208 [4:18:19<10:39:41, 42.93s/it] {'loss': 0.0019, 'grad_norm': 0.5196068047373481, 'learning_rate': 7.400662251655629e-07, 'completion_length': 88.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.08} 26%|██▌ | 314/1208 [4:18:19<10:39:41, 42.93s/it]Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 686178: cache has only 0 modules Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 687051: cache has only 0 modules 26%|██▌ | 315/1208 [4:18:49<9:43:58, 39.24s/it] {'loss': 0.0021, 'grad_norm': 4.902963990206593, 'learning_rate': 7.392384105960264e-07, 'completion_length': 80.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.0518798828125, 'clip_ratio': 0.0, 'epoch': 2.09} 26%|██▌ | 315/1208 [4:18:49<9:43:58, 39.24s/it]Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 687924: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 688797: cache has only 0 modules 26%|██▌ | 316/1208 [4:19:33<10:03:22, 40.59s/it] {'loss': 0.002, 'grad_norm': 8.616165790215799, 'learning_rate': 7.3841059602649e-07, 'completion_length': 97.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4355512708425522, 'kl': 0.049072265625, 'clip_ratio': 0.0, 'epoch': 2.09} 26%|██▌ | 316/1208 [4:19:33<10:03:22, 40.59s/it]Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 689670: cache has only 0 modules Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 690543: cache has only 0 modules 26%|██▌ | 317/1208 [4:20:07<9:32:16, 38.54s/it] {'loss': 0.0025, 'grad_norm': 1.0386011385210643, 'learning_rate': 7.375827814569537e-07, 'completion_length': 76.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0628662109375, 'clip_ratio': 0.0, 'epoch': 2.1} 26%|██▌ | 317/1208 [4:20:07<9:32:16, 38.54s/it]Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 691416: cache has only 0 modules Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 692289: cache has only 0 modules 26%|██▋ | 318/1208 [4:20:42<9:14:13, 37.36s/it] {'loss': 0.0022, 'grad_norm': 3.8500920981241564, 'learning_rate': 7.367549668874172e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.055908203125, 'clip_ratio': 0.0, 'epoch': 2.11} 26%|██▋ | 318/1208 [4:20:42<9:14:13, 37.36s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 693162: cache has only 0 modules Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 694035: cache has only 0 modules 26%|██▋ | 319/1208 [4:21:19<9:15:05, 37.46s/it] {'loss': 0.0022, 'grad_norm': 9.564833634339093, 'learning_rate': 7.359271523178807e-07, 'completion_length': 83.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.055419921875, 'clip_ratio': 0.0, 'epoch': 2.11} 26%|██▋ | 319/1208 [4:21:19<9:15:05, 37.46s/it]Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 694908: cache has only 0 modules Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 695781: cache has only 0 modules 26%|██▋ | 320/1208 [4:21:58<9:21:14, 37.92s/it] {'loss': 0.0017, 'grad_norm': 5.182335460830146, 'learning_rate': 7.350993377483443e-07, 'completion_length': 85.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.043212890625, 'clip_ratio': 0.0, 'epoch': 2.12} 26%|██▋ | 320/1208 [4:21:58<9:21:14, 37.92s/it]Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 696654: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 697527: cache has only 0 modules 27%|██▋ | 321/1208 [4:22:32<9:01:09, 36.61s/it] {'loss': 0.0019, 'grad_norm': 6.308217239353886, 'learning_rate': 7.34271523178808e-07, 'completion_length': 90.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0478515625, 'clip_ratio': 0.0, 'epoch': 2.13} 27%|██▋ | 321/1208 [4:22:32<9:01:09, 36.61s/it]Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 698400: cache has only 0 modules Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 699273: cache has only 0 modules 27%|██▋ | 322/1208 [4:23:02<8:30:33, 34.58s/it] {'loss': 0.0015, 'grad_norm': 6.160852256921588, 'learning_rate': 7.334437086092715e-07, 'completion_length': 82.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 2.13} 27%|██▋ | 322/1208 [4:23:02<8:30:33, 34.58s/it]Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 700146: cache has only 0 modules Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 701019: cache has only 0 modules 27%|██▋ | 323/1208 [4:23:38<8:39:01, 35.19s/it] {'loss': 0.0018, 'grad_norm': 4.371131471982293, 'learning_rate': 7.326158940397351e-07, 'completion_length': 87.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.0445556640625, 'clip_ratio': 0.0, 'epoch': 2.14} 27%|██▋ | 323/1208 [4:23:38<8:39:01, 35.19s/it]Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 701892: cache has only 0 modules Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 702765: cache has only 0 modules 27%|██▋ | 324/1208 [4:24:16<8:49:17, 35.92s/it] {'loss': 0.0024, 'grad_norm': 7.252953090475211, 'learning_rate': 7.317880794701986e-07, 'completion_length': 87.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0611572265625, 'clip_ratio': 0.0, 'epoch': 2.15} 27%|██▋ | 324/1208 [4:24:16<8:49:17, 35.92s/it]Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 703638: cache has only 0 modules Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 704511: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 705384: cache has only 0 modules [Step 324] loss_orig = 0.001946, loss_refine = 0.001933[Step 324] loss_orig = 0.001980, loss_refine = 0.002559 [Step 324] loss_orig = 0.001901, loss_refine = 0.001118[Step 324] loss_orig = 0.001266, loss_refine = 0.001765 [Step 324] loss_orig = 0.001999, loss_refine = 0.001361[Step 324] loss_orig = 0.002051, loss_refine = 0.002890 [Step 324] loss_orig = 0.002492, loss_refine = 0.002073 [Step 324] loss_orig = 0.001236, loss_refine = 0.002571 27%|██▋ | 325/1208 [4:25:12<10:16:38, 41.90s/it] {'loss': 0.0018, 'grad_norm': 4.349020431723374, 'learning_rate': 7.309602649006622e-07, 'completion_length': 93.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.0416666666666665, 'reward_std': 0.11785112818082173, 'kl': 0.0439453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 2.15} 27%|██▋ | 325/1208 [4:25:12<10:16:38, 41.90s/it]Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 706257: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 707130: cache has only 0 modules [Step 325] loss_orig = 0.000785, loss_refine = 1.501988[Step 325] loss_orig = 0.001468, loss_refine = -0.681214[Step 325] loss_orig = 0.001433, loss_refine = 1.502472[Step 325] loss_orig = 0.000601, loss_refine = -0.681186 [Step 325] loss_orig = 0.000702, loss_refine = -0.680539 [Step 325] loss_orig = 0.001197, loss_refine = -0.680758 [Step 325] loss_orig = 0.000612, loss_refine = 0.410523 [Step 325] loss_orig = 0.000975, loss_refine = -0.681370 Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 708003: cache has only 0 modules 27%|██▋ | 326/1208 [4:26:10<11:25:46, 46.65s/it] {'loss': 0.0011, 'grad_norm': 13.394040805722954, 'learning_rate': 7.301324503311258e-07, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.3053751389185588, 'kl': 0.0247802734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 2.16} 27%|██▋ | 326/1208 [4:26:10<11:25:46, 46.65s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 708876: cache has only 0 modules Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 709749: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [1014, 952]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 710622: cache has only 0 modules [Step 326] loss_orig = 0.000724, loss_refine = 0.935789 [Step 326] loss_orig = 0.000526, loss_refine = -0.934161 [Step 326] loss_orig = 0.006086, loss_refine = 0.935996 [Step 326] loss_orig = 0.001117, loss_refine = 0.937062 [Step 326] loss_orig = 0.000625, loss_refine = -0.934353[Step 326] loss_orig = 0.001110, loss_refine = -0.934516 [Step 326] loss_orig = 0.002360, loss_refine = -0.931990 [Step 326] loss_orig = 0.000660, loss_refine = 0.937014 27%|██▋ | 327/1208 [4:27:00<11:42:45, 47.86s/it] {'loss': 0.0013, 'grad_norm': 16.84700546690767, 'learning_rate': 7.293046357615894e-07, 'completion_length': 77.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.2960252861181895, 'kl': 0.036865234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.17} 27%|██▋ | 327/1208 [4:27:00<11:42:45, 47.86s/it]Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 711495: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 712368: cache has only 0 modules 27%|██▋ | 328/1208 [4:27:36<10:48:37, 44.22s/it] {'loss': 0.0017, 'grad_norm': 19.444237669293436, 'learning_rate': 7.284768211920528e-07, 'completion_length': 81.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4629100561141968, 'kl': 0.0423583984375, 'clip_ratio': 0.0, 'epoch': 2.17} 27%|██▋ | 328/1208 [4:27:36<10:48:37, 44.22s/it]Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 713241: cache has only 0 modules Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 714114: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 714987: cache has only 0 modules [Step 328] loss_orig = -0.352320, loss_refine = 0.001399[Step 328] loss_orig = -0.351722, loss_refine = 0.004523[Step 328] loss_orig = -0.352034, loss_refine = 0.002080[Step 328] loss_orig = -0.351406, loss_refine = 0.001722 [Step 328] loss_orig = -0.352328, loss_refine = 0.001357[Step 328] loss_orig = 2.476053, loss_refine = 0.001523[Step 328] loss_orig = -0.351755, loss_refine = 0.001238 [Step 328] loss_orig = -0.352123, loss_refine = 0.001332 27%|██▋ | 329/1208 [4:28:34<11:46:46, 48.24s/it] {'loss': 0.002, 'grad_norm': 7.441970654872059, 'learning_rate': 7.276490066225165e-07, 'completion_length': 99.29166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.046630859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.18} 27%|██▋ | 329/1208 [4:28:34<11:46:46, 48.24s/it]Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 715860: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 716733: cache has only 0 modules [Step 329] loss_orig = 0.000773, loss_refine = 0.938690 [Step 329] loss_orig = 0.000854, loss_refine = -0.934313 [Step 329] loss_orig = 0.001584, loss_refine = 0.936857[Step 329] loss_orig = 0.000848, loss_refine = -0.933388 [Step 329] loss_orig = 0.003456, loss_refine = -0.932746 [Step 329] loss_orig = 0.001188, loss_refine = 0.937457 [Step 329] loss_orig = 0.001516, loss_refine = 0.936562 [Step 329] loss_orig = 0.002236, loss_refine = -0.932826 Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 717606: cache has only 0 modules 27%|██▋ | 330/1208 [4:29:30<12:22:24, 50.73s/it] {'loss': 0.002, 'grad_norm': 7.72682471702684, 'learning_rate': 7.268211920529801e-07, 'completion_length': 103.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.3506905436515808, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.19} 27%|██▋ | 330/1208 [4:29:30<12:22:24, 50.73s/it]Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 718479: cache has only 0 modules Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 719352: cache has only 0 modules 27%|██▋ | 331/1208 [4:30:05<11:11:05, 45.91s/it] {'loss': 0.0029, 'grad_norm': 21.20580009813232, 'learning_rate': 7.259933774834437e-07, 'completion_length': 82.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0731201171875, 'clip_ratio': 0.0, 'epoch': 2.19} 27%|██▋ | 331/1208 [4:30:05<11:11:05, 45.91s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 720225: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1286, 647]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 721098: cache has only 0 modules [Step 331] loss_orig = 0.000738, loss_refine = -2.182702 [Step 331] loss_orig = 0.001598, loss_refine = 0.506079 [Step 331] loss_orig = 0.000947, loss_refine = 0.505118[Step 331] loss_orig = 0.001190, loss_refine = -0.838608 [Step 331] loss_orig = 0.000773, loss_refine = 0.504892 [Step 331] loss_orig = 0.001191, loss_refine = 0.505253[Step 331] loss_orig = 0.000896, loss_refine = 0.504954 [Step 331] loss_orig = 0.000802, loss_refine = 0.506413 Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 721971: cache has only 0 modules 27%|██▋ | 332/1208 [4:31:02<11:58:36, 49.22s/it] {'loss': 0.0029, 'grad_norm': 20.771778649950182, 'learning_rate': 7.251655629139073e-07, 'completion_length': 106.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4023112853368123, 'kl': 0.0673828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.2} 27%|██▋ | 332/1208 [4:31:02<11:58:36, 49.22s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 722844: cache has only 0 modules Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 723717: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 724590: cache has only 0 modules [Step 332] loss_orig = 0.001927, loss_refine = 0.541573 [Step 332] loss_orig = 0.002722, loss_refine = 0.542864[Step 332] loss_orig = 0.001517, loss_refine = 0.543816 [Step 332] loss_orig = 0.001973, loss_refine = -1.616662[Step 332] loss_orig = 0.002897, loss_refine = -1.614823 [Step 332] loss_orig = 0.012466, loss_refine = 0.541301 [Step 332] loss_orig = 0.001278, loss_refine = 0.541519 [Step 332] loss_orig = 0.002141, loss_refine = 0.542475 28%|██▊ | 333/1208 [4:31:53<12:05:30, 49.75s/it] {'loss': 0.0024, 'grad_norm': 8.14110191378139, 'learning_rate': 7.243377483443708e-07, 'completion_length': 94.83333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.0667724609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.21} 28%|██▊ | 333/1208 [4:31:53<12:05:30, 49.75s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 725463: cache has only 0 modules Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 726336: cache has only 0 modules 28%|██▊ | 334/1208 [4:32:35<11:30:23, 47.40s/it] {'loss': 0.0015, 'grad_norm': 10.06992204787649, 'learning_rate': 7.235099337748344e-07, 'completion_length': 101.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 2.21} 28%|██▊ | 334/1208 [4:32:35<11:30:23, 47.40s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 727209: cache has only 0 modules Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 728082: cache has only 0 modules 28%|██▊ | 335/1208 [4:33:23<11:32:21, 47.59s/it] {'loss': 0.0021, 'grad_norm': 9.488509458674493, 'learning_rate': 7.226821192052979e-07, 'completion_length': 106.5, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.05242919921875, 'clip_ratio': 0.0, 'epoch': 2.22} 28%|██▊ | 335/1208 [4:33:23<11:32:21, 47.59s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 728955: cache has only 0 modules Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 729828: cache has only 0 modules 28%|██▊ | 336/1208 [4:33:59<10:42:12, 44.19s/it] {'loss': 0.0021, 'grad_norm': 0.3467945320311397, 'learning_rate': 7.218543046357616e-07, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.052001953125, 'clip_ratio': 0.0, 'epoch': 2.23} 28%|██▊ | 336/1208 [4:33:59<10:42:12, 44.19s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 730701: cache has only 0 modules Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 731574: cache has only 0 modules 28%|██▊ | 337/1208 [4:34:35<10:08:01, 41.88s/it] {'loss': 0.0027, 'grad_norm': 7.0011617709794125, 'learning_rate': 7.210264900662252e-07, 'completion_length': 84.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0675048828125, 'clip_ratio': 0.0, 'epoch': 2.23} 28%|██▊ | 337/1208 [4:34:35<10:08:01, 41.88s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 732447: cache has only 0 modules Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 733320: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sign in - Google Accounts'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 734193: cache has only 0 modules [Step 337] loss_orig = 0.002196, loss_refine = -0.499722[Step 337] loss_orig = 0.000880, loss_refine = 0.841026[Step 337] loss_orig = 0.001107, loss_refine = 0.842980[Step 337] loss_orig = 0.001501, loss_refine = 0.843674 [Step 337] loss_orig = 0.002243, loss_refine = -0.500879 [Step 337] loss_orig = 0.002457, loss_refine = -1.846012 [Step 337] loss_orig = 0.001338, loss_refine = -0.501873[Step 337] loss_orig = 0.000885, loss_refine = 0.841066 28%|██▊ | 338/1208 [4:35:28<10:52:46, 45.02s/it] {'loss': 0.0026, 'grad_norm': 26.95626672809984, 'learning_rate': 7.201986754966886e-07, 'completion_length': 101.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.3658590614795685, 'kl': 0.0538330078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.24} 28%|██▊ | 338/1208 [4:35:28<10:52:46, 45.02s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 735066: cache has only 0 modules Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 735939: cache has only 0 modules 28%|██▊ | 339/1208 [4:36:12<10:49:13, 44.83s/it] {'loss': 0.0018, 'grad_norm': 10.145116659162033, 'learning_rate': 7.193708609271522e-07, 'completion_length': 98.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.5260358154773712, 'kl': 0.0457763671875, 'clip_ratio': 0.0, 'epoch': 2.25} 28%|██▊ | 339/1208 [4:36:12<10:49:13, 44.83s/it]Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 736812: cache has only 0 modules Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 737685: cache has only 0 modules 28%|██▊ | 340/1208 [4:36:46<9:59:29, 41.44s/it] {'loss': 0.0018, 'grad_norm': 5.660122401748402, 'learning_rate': 7.185430463576159e-07, 'completion_length': 88.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.044677734375, 'clip_ratio': 0.0, 'epoch': 2.25} 28%|██▊ | 340/1208 [4:36:46<9:59:29, 41.44s/it]Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 738558: cache has only 0 modules Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 739431: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 740304: cache has only 0 modules [Step 340] loss_orig = 2.475762, loss_refine = 0.002662 [Step 340] loss_orig = -0.350383, loss_refine = 0.044349 [Step 340] loss_orig = -0.352328, loss_refine = 0.001581 [Step 340] loss_orig = -0.352240, loss_refine = 0.001852[Step 340] loss_orig = -0.352450, loss_refine = 0.002178 [Step 340] loss_orig = -0.352226, loss_refine = 0.002923[Step 340] loss_orig = -0.351780, loss_refine = 0.001845 [Step 340] loss_orig = -0.351735, loss_refine = 0.001313 28%|██▊ | 341/1208 [4:37:44<11:11:40, 46.48s/it] {'loss': 0.0048, 'grad_norm': 1.9503664458947945, 'learning_rate': 7.177152317880795e-07, 'completion_length': 103.91666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.26} 28%|██▊ | 341/1208 [4:37:44<11:11:40, 46.48s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 741177: cache has only 0 modules Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 742050: cache has only 0 modules 28%|██▊ | 342/1208 [4:38:19<10:19:54, 42.95s/it] {'loss': 0.0018, 'grad_norm': 6.271623620065582, 'learning_rate': 7.16887417218543e-07, 'completion_length': 89.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.04443359375, 'clip_ratio': 0.0, 'epoch': 2.26} 28%|██▊ | 342/1208 [4:38:19<10:19:54, 42.95s/it]Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 742923: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'setting up airpods connection'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 743796: cache has only 0 modules [Step 342] loss_orig = -0.352031, loss_refine = 0.000850 [Step 342] loss_orig = -0.351806, loss_refine = 0.005517[Step 342] loss_orig = -0.352588, loss_refine = 0.000945 [Step 342] loss_orig = 2.475782, loss_refine = 0.001825 [Step 342] loss_orig = -0.352609, loss_refine = 0.000920 [Step 342] loss_orig = -0.351498, loss_refine = 0.000867 [Step 342] loss_orig = -0.351916, loss_refine = 0.000870 [Step 342] loss_orig = -0.348443, loss_refine = 0.002080 Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 744669: cache has only 0 modules 28%|██▊ | 343/1208 [4:39:38<12:58:39, 54.01s/it] {'loss': 0.002, 'grad_norm': 7.313490355863767, 'learning_rate': 7.160596026490066e-07, 'completion_length': 116.66666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.0416666666666665, 'reward_std': 0.4837101896603902, 'kl': 0.0523681640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 2.27} 28%|██▊ | 343/1208 [4:39:38<12:58:39, 54.01s/it]Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 745542: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 746415: cache has only 0 modules 28%|██▊ | 344/1208 [4:40:24<12:22:58, 51.60s/it] {'loss': 0.0025, 'grad_norm': 8.499741442045833, 'learning_rate': 7.152317880794702e-07, 'completion_length': 114.375, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.875, 'reward': 2.125, 'reward_std': 0.7891046404838562, 'kl': 0.0625, 'clip_ratio': 0.0, 'epoch': 2.28} 28%|██▊ | 344/1208 [4:40:24<12:22:58, 51.60s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 747288: cache has only 0 modules Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 748161: cache has only 0 modules 29%|██▊ | 345/1208 [4:41:00<11:11:32, 46.69s/it] {'loss': 0.0024, 'grad_norm': 5.194340340821357, 'learning_rate': 7.144039735099337e-07, 'completion_length': 94.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.060791015625, 'clip_ratio': 0.0, 'epoch': 2.28} 29%|██▊ | 345/1208 [4:41:00<11:11:32, 46.69s/it]Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 749034: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 749907: cache has only 0 modules 29%|██▊ | 346/1208 [4:41:39<10:37:50, 44.40s/it] {'loss': 0.0014, 'grad_norm': 0.3272379880403988, 'learning_rate': 7.135761589403973e-07, 'completion_length': 95.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'epoch': 2.29} 29%|██▊ | 346/1208 [4:41:39<10:37:50, 44.40s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 750780: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 751653: cache has only 0 modules 29%|██▊ | 347/1208 [4:42:14<9:56:51, 41.59s/it] {'loss': 0.0015, 'grad_norm': 8.909171832355185, 'learning_rate': 7.127483443708609e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0382080078125, 'clip_ratio': 0.0, 'epoch': 2.3} 29%|██▊ | 347/1208 [4:42:14<9:56:51, 41.59s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 752526: cache has only 0 modules Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 753399: cache has only 0 modules 29%|██▉ | 348/1208 [4:43:04<10:31:55, 44.09s/it] {'loss': 0.0028, 'grad_norm': 40.97240580524306, 'learning_rate': 7.119205298013245e-07, 'completion_length': 101.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0711669921875, 'clip_ratio': 0.0, 'epoch': 2.3} 29%|██▉ | 348/1208 [4:43:04<10:31:55, 44.09s/it]Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 754272: cache has only 0 modules Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 755145: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Copilot (Ctrl+Shift+.)'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 756018: cache has only 0 modules [Step 348] loss_orig = 0.003503, loss_refine = 0.728503 [Step 348] loss_orig = 0.002367, loss_refine = 0.726942[Step 348] loss_orig = 0.002106, loss_refine = -1.204522 [Step 348] loss_orig = 0.001707, loss_refine = -1.205702 [Step 348] loss_orig = 0.003836, loss_refine = 0.725834 [Step 348] loss_orig = 0.002143, loss_refine = 0.726420 [Step 348] loss_orig = 0.001966, loss_refine = 0.726568 [Step 348] loss_orig = 0.002390, loss_refine = -1.206091 29%|██▉ | 349/1208 [4:44:08<11:56:55, 50.08s/it] {'loss': 0.0016, 'grad_norm': 17.965296206830654, 'learning_rate': 7.11092715231788e-07, 'completion_length': 96.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.0438232421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.31} 29%|██▉ | 349/1208 [4:44:08<11:56:55, 50.08s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 756891: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 757764: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [602, 1410]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 758637: cache has only 0 modules [Step 349] loss_orig = 0.001393, loss_refine = 0.936536 [Step 349] loss_orig = 0.004614, loss_refine = 0.936132[Step 349] loss_orig = 0.001413, loss_refine = 0.936054 [Step 349] loss_orig = 0.002509, loss_refine = 0.936140 [Step 349] loss_orig = 0.004136, loss_refine = -0.930649[Step 349] loss_orig = 0.001803, loss_refine = -0.933245 [Step 349] loss_orig = 0.002073, loss_refine = -0.933905 [Step 349] loss_orig = 0.001083, loss_refine = -0.933235 29%|██▉ | 350/1208 [4:45:01<12:08:52, 50.97s/it] {'loss': 0.0022, 'grad_norm': 21.518701236834794, 'learning_rate': 7.102649006622516e-07, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.2960252861181895, 'kl': 0.0631103515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.32} 29%|██▉ | 350/1208 [4:45:01<12:08:52, 50.97s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 759510: cache has only 0 modules Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 760383: cache has only 0 modules 29%|██▉ | 351/1208 [4:45:37<11:03:37, 46.46s/it] {'loss': 0.0014, 'grad_norm': 5.698432666344891, 'learning_rate': 7.094370860927153e-07, 'completion_length': 86.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'epoch': 2.32} 29%|██▉ | 351/1208 [4:45:37<11:03:37, 46.46s/it]Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 761256: cache has only 0 modules Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 762129: cache has only 0 modules 29%|██▉ | 352/1208 [4:46:13<10:18:45, 43.37s/it] {'loss': 0.0024, 'grad_norm': 7.180700922076131, 'learning_rate': 7.086092715231787e-07, 'completion_length': 93.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.0589599609375, 'clip_ratio': 0.0, 'epoch': 2.33} 29%|██▉ | 352/1208 [4:46:13<10:18:45, 43.37s/it]Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 763002: cache has only 0 modules Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 763875: cache has only 0 modules 29%|██▉ | 353/1208 [4:46:57<10:21:40, 43.63s/it] {'loss': 0.0025, 'grad_norm': 26.20797193851708, 'learning_rate': 7.077814569536423e-07, 'completion_length': 98.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.063720703125, 'clip_ratio': 0.0, 'epoch': 2.34} 29%|██▉ | 353/1208 [4:46:57<10:21:40, 43.63s/it]Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 764748: cache has only 0 modules Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 765621: cache has only 0 modules 29%|██▉ | 354/1208 [4:47:52<11:09:06, 47.01s/it] {'loss': 0.0024, 'grad_norm': 4.78426142487205, 'learning_rate': 7.06953642384106e-07, 'completion_length': 105.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.7891046404838562, 'kl': 0.060302734375, 'clip_ratio': 0.0, 'epoch': 2.34} 29%|██▉ | 354/1208 [4:47:52<11:09:06, 47.01s/it]Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 766494: cache has only 0 modules Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 767367: cache has only 0 modules 29%|██▉ | 355/1208 [4:48:30<10:29:43, 44.29s/it] {'loss': 0.0014, 'grad_norm': 0.442753439050375, 'learning_rate': 7.061258278145695e-07, 'completion_length': 93.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0350341796875, 'clip_ratio': 0.0, 'epoch': 2.35} 29%|██▉ | 355/1208 [4:48:30<10:29:43, 44.29s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 768240: cache has only 0 modules Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 769113: cache has only 0 modules 29%|██▉ | 356/1208 [4:49:03<9:40:08, 40.85s/it] {'loss': 0.0025, 'grad_norm': 16.38669401550925, 'learning_rate': 7.052980132450331e-07, 'completion_length': 84.1875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.5345224738121033, 'kl': 0.0618896484375, 'clip_ratio': 0.0, 'epoch': 2.36} 29%|██▉ | 356/1208 [4:49:03<9:40:08, 40.85s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 769986: cache has only 0 modules Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 770859: cache has only 0 modules 30%|██▉ | 357/1208 [4:49:41<9:29:08, 40.13s/it] {'loss': 0.0015, 'grad_norm': 0.5323738809592651, 'learning_rate': 7.044701986754966e-07, 'completion_length': 100.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03662109375, 'clip_ratio': 0.0, 'epoch': 2.36} 30%|██▉ | 357/1208 [4:49:41<9:29:08, 40.13s/it]Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 771732: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 772605: cache has only 0 modules 30%|██▉ | 358/1208 [4:50:24<9:41:23, 41.04s/it] {'loss': 0.0023, 'grad_norm': 7.484607418755059, 'learning_rate': 7.036423841059603e-07, 'completion_length': 99.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.056884765625, 'clip_ratio': 0.0, 'epoch': 2.37} 30%|██▉ | 358/1208 [4:50:24<9:41:23, 41.04s/it]Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 773478: cache has only 0 modules Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 774351: cache has only 0 modules 30%|██▉ | 359/1208 [4:50:59<9:14:28, 39.19s/it] {'loss': 0.0014, 'grad_norm': 4.615826518961802, 'learning_rate': 7.028145695364238e-07, 'completion_length': 83.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.03594970703125, 'clip_ratio': 0.0, 'epoch': 2.38} 30%|██▉ | 359/1208 [4:50:59<9:14:28, 39.19s/it]Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 775224: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 776097: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 776970: cache has only 0 modules [Step 359] loss_orig = 0.001081, loss_refine = -0.723244[Step 359] loss_orig = 0.002862, loss_refine = -0.723276 [Step 359] loss_orig = 0.000925, loss_refine = 1.208712 [Step 359] loss_orig = 0.001387, loss_refine = -0.722951 [Step 359] loss_orig = 0.000884, loss_refine = 1.209024 [Step 359] loss_orig = 0.001061, loss_refine = 1.209571 [Step 359] loss_orig = 0.002295, loss_refine = -0.722340 [Step 359] loss_orig = 0.003067, loss_refine = -0.722973 30%|██▉ | 360/1208 [4:51:57<10:31:05, 44.65s/it] {'loss': 0.0027, 'grad_norm': 16.57962414476717, 'learning_rate': 7.019867549668874e-07, 'completion_length': 96.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.3450327714284261, 'kl': 0.069580078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 2.38} 30%|██▉ | 360/1208 [4:51:57<10:31:05, 44.65s/it]Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 777843: cache has only 0 modules Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 778716: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functional icon'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 779589: cache has only 0 modules [Step 360] loss_orig = 0.001353, loss_refine = -1.617178[Step 360] loss_orig = 0.001813, loss_refine = 0.540707[Step 360] loss_orig = 0.002665, loss_refine = 0.541210[Step 360] loss_orig = 0.001179, loss_refine = 0.541451 [Step 360] loss_orig = 0.000950, loss_refine = 0.540702[Step 360] loss_orig = 0.004141, loss_refine = 0.542453[Step 360] loss_orig = 0.001045, loss_refine = -1.619355 [Step 360] loss_orig = 0.002288, loss_refine = 0.540857 30%|██▉ | 361/1208 [4:52:44<10:39:51, 45.33s/it] {'loss': 0.0014, 'grad_norm': 6.072692440211207, 'learning_rate': 7.011589403973509e-07, 'completion_length': 86.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.0408935546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.39} 30%|██▉ | 361/1208 [4:52:44<10:39:51, 45.33s/it]Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 780462: cache has only 0 modules Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 781335: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 782208: cache has only 0 modules [Step 361] loss_orig = 0.001155, loss_refine = 0.728839[Step 361] loss_orig = 0.003239, loss_refine = -1.205319[Step 361] loss_orig = 0.001776, loss_refine = -1.205302[Step 361] loss_orig = 0.001687, loss_refine = 0.729079[Step 361] loss_orig = 0.001111, loss_refine = -1.205345 [Step 361] loss_orig = 0.001155, loss_refine = 0.726205 [Step 361] loss_orig = 0.001479, loss_refine = 0.726599 [Step 361] loss_orig = 0.002662, loss_refine = 0.725662 30%|██▉ | 362/1208 [4:53:46<11:50:31, 50.39s/it] {'loss': 0.0026, 'grad_norm': 15.227293345672662, 'learning_rate': 7.003311258278145e-07, 'completion_length': 111.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.3450327714284261, 'kl': 0.05419921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.4} 30%|██▉ | 362/1208 [4:53:46<11:50:31, 50.39s/it]Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 783081: cache has only 0 modules Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 783954: cache has only 0 modules 30%|███ | 363/1208 [4:54:20<10:42:24, 45.61s/it] {'loss': 0.0012, 'grad_norm': 5.568087196554229, 'learning_rate': 6.995033112582781e-07, 'completion_length': 87.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.02972412109375, 'clip_ratio': 0.0, 'epoch': 2.4} 30%|███ | 363/1208 [4:54:20<10:42:24, 45.61s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 784827: cache has only 0 modules Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 785700: cache has only 0 modules 30%|███ | 364/1208 [4:54:58<10:09:54, 43.36s/it] {'loss': 0.0016, 'grad_norm': 0.3191057661498876, 'learning_rate': 6.986754966887417e-07, 'completion_length': 87.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04022216796875, 'clip_ratio': 0.0, 'epoch': 2.41} 30%|███ | 364/1208 [4:54:58<10:09:54, 43.36s/it]Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 786573: cache has only 0 modules Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 787446: cache has only 0 modules 30%|███ | 365/1208 [4:55:37<9:48:02, 41.85s/it] {'loss': 0.0014, 'grad_norm': 6.643505920419802, 'learning_rate': 6.978476821192054e-07, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03472900390625, 'clip_ratio': 0.0, 'epoch': 2.42} 30%|███ | 365/1208 [4:55:37<9:48:02, 41.85s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 788319: cache has only 0 modules Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 789192: cache has only 0 modules 30%|███ | 366/1208 [4:56:13<9:23:29, 40.15s/it] {'loss': 0.0011, 'grad_norm': 0.2330452754452818, 'learning_rate': 6.970198675496688e-07, 'completion_length': 84.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02850341796875, 'clip_ratio': 0.0, 'epoch': 2.42} 30%|███ | 366/1208 [4:56:13<9:23:29, 40.15s/it]Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 790065: cache has only 0 modules Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 790938: cache has only 0 modules 30%|███ | 367/1208 [4:56:50<9:11:22, 39.34s/it] {'loss': 0.0033, 'grad_norm': 0.6851367971203077, 'learning_rate': 6.961920529801324e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0816650390625, 'clip_ratio': 0.0, 'epoch': 2.43} 30%|███ | 367/1208 [4:56:50<9:11:22, 39.34s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 791811: cache has only 0 modules Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 792684: cache has only 0 modules 30%|███ | 368/1208 [4:57:30<9:12:13, 39.44s/it] {'loss': 0.0034, 'grad_norm': 5.348274934666787, 'learning_rate': 6.95364238410596e-07, 'completion_length': 105.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.0848388671875, 'clip_ratio': 0.0, 'epoch': 2.44} 30%|███ | 368/1208 [4:57:30<9:12:13, 39.44s/it]Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 793557: cache has only 0 modules Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 794430: cache has only 0 modules 31%|███ | 369/1208 [4:58:09<9:11:33, 39.44s/it] {'loss': 0.0021, 'grad_norm': 14.838983963118213, 'learning_rate': 6.945364238410596e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.052734375, 'clip_ratio': 0.0, 'epoch': 2.44} 31%|███ | 369/1208 [4:58:09<9:11:33, 39.44s/it]Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 795303: cache has only 0 modules Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 796176: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 797049: cache has only 0 modules [Step 369] loss_orig = 0.001368, loss_refine = 0.000811[Step 369] loss_orig = 0.000889, loss_refine = 0.001146 [Step 369] loss_orig = 0.001816, loss_refine = 0.001304 [Step 369] loss_orig = 0.001764, loss_refine = 0.001400 [Step 369] loss_orig = 0.001316, loss_refine = 0.001294[Step 369] loss_orig = 0.003985, loss_refine = 0.001095 [Step 369] loss_orig = 0.001275, loss_refine = 0.001036 [Step 369] loss_orig = 0.001767, loss_refine = 0.001421 31%|███ | 370/1208 [4:59:04<10:13:10, 43.90s/it] {'loss': 0.0019, 'grad_norm': 0.38977444946924816, 'learning_rate': 6.937086092715232e-07, 'completion_length': 83.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.0538330078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.45} 31%|███ | 370/1208 [4:59:04<10:13:10, 43.90s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 797922: cache has only 0 modules Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 798795: cache has only 0 modules 31%|███ | 371/1208 [4:59:44<9:58:42, 42.92s/it] {'loss': 0.0033, 'grad_norm': 6.999695220439647, 'learning_rate': 6.928807947019867e-07, 'completion_length': 95.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.08203125, 'clip_ratio': 0.0, 'epoch': 2.46} 31%|███ | 371/1208 [4:59:44<9:58:42, 42.92s/it]Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 799668: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 800541: cache has only 0 modules 31%|███ | 372/1208 [5:00:15<9:07:45, 39.31s/it] {'loss': 0.0024, 'grad_norm': 6.268165705185522, 'learning_rate': 6.920529801324502e-07, 'completion_length': 83.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.5303300768136978, 'kl': 0.0594482421875, 'clip_ratio': 0.0, 'epoch': 2.46} 31%|███ | 372/1208 [5:00:15<9:07:45, 39.31s/it]Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 801414: cache has only 0 modules Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 802287: cache has only 0 modules 31%|███ | 373/1208 [5:00:55<9:07:45, 39.36s/it] {'loss': 0.0015, 'grad_norm': 18.357563634355202, 'learning_rate': 6.912251655629139e-07, 'completion_length': 95.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.037841796875, 'clip_ratio': 0.0, 'epoch': 2.47} 31%|███ | 373/1208 [5:00:55<9:07:45, 39.36s/it]Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 803160: cache has only 0 modules Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 804033: cache has only 0 modules 31%|███ | 374/1208 [5:01:46<9:58:09, 43.03s/it] {'loss': 0.0018, 'grad_norm': 11.95676105064533, 'learning_rate': 6.903973509933775e-07, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0447998046875, 'clip_ratio': 0.0, 'epoch': 2.48} 31%|███ | 374/1208 [5:01:46<9:58:09, 43.03s/it]Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 804906: cache has only 0 modules Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 805779: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Tray Input Indicator - Chinese (Simplified, China)'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box diff coord reward errorcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 806652: cache has only 0 modules [Step 374] loss_orig = 0.001165, loss_refine = -0.880688[Step 374] loss_orig = 0.001770, loss_refine = -0.879621 [Step 374] loss_orig = 0.001964, loss_refine = 0.129076 [Step 374] loss_orig = 0.001797, loss_refine = 0.128334 [Step 374] loss_orig = 0.001686, loss_refine = 0.129699 [Step 374] loss_orig = 0.001927, loss_refine = -0.881569 [Step 374] loss_orig = 0.001118, loss_refine = 2.146269 [Step 374] loss_orig = 0.001669, loss_refine = 0.129038 31%|███ | 375/1208 [5:03:00<12:03:19, 52.10s/it] {'loss': 0.002, 'grad_norm': 7.6011296266968165, 'learning_rate': 6.895695364238411e-07, 'completion_length': 117.95833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.1666666666666665, 'reward_std': 0.5028601288795471, 'kl': 0.0384521484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.48} 31%|███ | 375/1208 [5:03:00<12:03:19, 52.10s/it]Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 807525: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 808398: cache has only 0 modules 31%|███ | 376/1208 [5:03:36<10:56:34, 47.35s/it] {'loss': 0.0023, 'grad_norm': 16.235802833953795, 'learning_rate': 6.887417218543045e-07, 'completion_length': 94.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4629100561141968, 'kl': 0.05810546875, 'clip_ratio': 0.0, 'epoch': 2.49} 31%|███ | 376/1208 [5:03:36<10:56:34, 47.35s/it]Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 809271: cache has only 0 modules Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 810144: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 811017: cache has only 0 modules [Step 376] loss_orig = 0.000847, loss_refine = -0.352703[Step 376] loss_orig = 0.000792, loss_refine = -0.351937[Step 376] loss_orig = 0.000539, loss_refine = 2.475796[Step 376] loss_orig = 0.000567, loss_refine = -0.352488 [Step 376] loss_orig = 0.000714, loss_refine = -0.352562 [Step 376] loss_orig = 0.000572, loss_refine = -0.352573 [Step 376] loss_orig = 0.001138, loss_refine = -0.351503 [Step 376] loss_orig = 0.000939, loss_refine = -0.351036 31%|███ | 377/1208 [5:04:24<11:00:06, 47.66s/it] {'loss': 0.002, 'grad_norm': 14.610432539961229, 'learning_rate': 6.879139072847682e-07, 'completion_length': 79.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.4082186420758565, 'kl': 0.043212890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 2.5} 31%|███ | 377/1208 [5:04:24<11:00:06, 47.66s/it]Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 811890: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 812763: cache has only 0 modules [Step 377] loss_orig = 0.001452, loss_refine = -0.836767 [Step 377] loss_orig = 0.002321, loss_refine = 0.507160[Step 377] loss_orig = 0.001821, loss_refine = -0.838112 [Step 377] loss_orig = 0.003501, loss_refine = 0.508614 [Step 377] loss_orig = 0.002776, loss_refine = 1.924883 [Step 377] loss_orig = 0.001281, loss_refine = -0.838458 [Step 377] loss_orig = 0.001521, loss_refine = 0.505751 [Step 377] loss_orig = 0.001600, loss_refine = -0.836819 Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 813636: cache has only 0 modules 31%|███▏ | 378/1208 [5:05:24<11:48:28, 51.21s/it] {'loss': 0.0069, 'grad_norm': 6.40618071886674, 'learning_rate': 6.870860927152318e-07, 'completion_length': 94.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4583333333333335, 'reward_std': 0.24800793329874674, 'kl': 0.0472412109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.5} 31%|███▏ | 378/1208 [5:05:24<11:48:28, 51.21s/it]Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 814509: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 815382: cache has only 0 modules [Step 378] loss_orig = 0.002024, loss_refine = 0.354364[Step 378] loss_orig = 0.001667, loss_refine = 0.355073[Step 378] loss_orig = 0.001874, loss_refine = -2.473480 [Step 378] loss_orig = 0.002402, loss_refine = 0.354593[Step 378] loss_orig = 0.001513, loss_refine = 0.354210[Step 378] loss_orig = 0.001377, loss_refine = 0.354141[Step 378] loss_orig = 0.001662, loss_refine = 0.354774 [Step 378] loss_orig = 0.001022, loss_refine = 0.354658 Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 816255: cache has only 0 modules 31%|███▏ | 379/1208 [5:06:16<11:53:05, 51.61s/it] {'loss': 0.0017, 'grad_norm': 8.074987785680225, 'learning_rate': 6.862582781456953e-07, 'completion_length': 94.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.0506591796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.51} 31%|███▏ | 379/1208 [5:06:16<11:53:05, 51.61s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 817128: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'flod this content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 818001: cache has only 0 modules [Step 379] loss_orig = 0.001754, loss_refine = 0.937928[Step 379] loss_orig = 0.003234, loss_refine = 0.936012 [Step 379] loss_orig = 0.001230, loss_refine = -0.933303 [Step 379] loss_orig = 0.001264, loss_refine = -0.933135 [Step 379] loss_orig = 0.001241, loss_refine = -0.933875 [Step 379] loss_orig = 0.001089, loss_refine = 0.937328 [Step 379] loss_orig = 0.002511, loss_refine = 0.936530 [Step 379] loss_orig = 0.002498, loss_refine = -0.932786 Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 818874: cache has only 0 modules 31%|███▏ | 380/1208 [5:07:23<12:54:39, 56.14s/it] {'loss': 0.0019, 'grad_norm': 10.699487553829414, 'learning_rate': 6.854304635761589e-07, 'completion_length': 99.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.33247750997543335, 'kl': 0.047607421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.52} 31%|███▏ | 380/1208 [5:07:23<12:54:39, 56.14s/it]Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 819747: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command '1'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 820620: cache has only 0 modules [Step 380] loss_orig = 0.000590, loss_refine = 0.540949[Step 380] loss_orig = 0.002445, loss_refine = 0.541237[Step 380] loss_orig = 0.003987, loss_refine = -1.618914 [Step 380] loss_orig = 0.000999, loss_refine = 0.540957[Step 380] loss_orig = 0.002601, loss_refine = 0.540928 [Step 380] loss_orig = 0.001011, loss_refine = 0.540453 [Step 380] loss_orig = 0.002342, loss_refine = -1.618015 [Step 380] loss_orig = 0.002289, loss_refine = 0.541231 Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 821493: cache has only 0 modules 32%|███▏ | 381/1208 [5:08:18<12:48:53, 55.78s/it] {'loss': 0.0014, 'grad_norm': 3.9078154621357237, 'learning_rate': 6.846026490066225e-07, 'completion_length': 101.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.0469970703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.52} 32%|███▏ | 381/1208 [5:08:18<12:48:53, 55.78s/it]Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 822366: cache has only 0 modules Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 823239: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1381, 614]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 824112: cache has only 0 modules [Step 381] loss_orig = 0.001282, loss_refine = 0.542018 [Step 381] loss_orig = 0.001704, loss_refine = 0.542164[Step 381] loss_orig = 0.001811, loss_refine = 0.541321 [Step 381] loss_orig = 0.003532, loss_refine = 0.541769 [Step 381] loss_orig = 0.002803, loss_refine = 0.542056[Step 381] loss_orig = 0.001235, loss_refine = 0.541838 [Step 381] loss_orig = 0.001424, loss_refine = -1.617810 [Step 381] loss_orig = 0.001222, loss_refine = -1.618772 32%|███▏ | 382/1208 [5:09:21<13:17:26, 57.93s/it] {'loss': 0.0025, 'grad_norm': 57.08549039958253, 'learning_rate': 6.83774834437086e-07, 'completion_length': 106.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.2083333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.0634765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.53} 32%|███▏ | 382/1208 [5:09:21<13:17:26, 57.93s/it]Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 824985: cache has only 0 modules Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 825858: cache has only 0 modules 32%|███▏ | 383/1208 [5:10:00<12:01:06, 52.44s/it] {'loss': 0.0017, 'grad_norm': 5.653825407679323, 'learning_rate': 6.829470198675496e-07, 'completion_length': 104.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0416259765625, 'clip_ratio': 0.0, 'epoch': 2.54} 32%|███▏ | 383/1208 [5:10:00<12:01:06, 52.44s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 826731: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 827604: cache has only 0 modules 32%|███▏ | 384/1208 [5:10:52<11:55:34, 52.11s/it] {'loss': 0.0014, 'grad_norm': 12.15163987469804, 'learning_rate': 6.821192052980133e-07, 'completion_length': 125.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.5260358154773712, 'kl': 0.0352783203125, 'clip_ratio': 0.0, 'epoch': 2.54} 32%|███▏ | 384/1208 [5:10:52<11:55:34, 52.11s/it]Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 828477: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 829350: cache has only 0 modules [Step 384] loss_orig = 0.001408, loss_refine = -0.933997 [Step 384] loss_orig = 0.002899, loss_refine = -0.933688 [Step 384] loss_orig = 0.001795, loss_refine = 0.937686 [Step 384] loss_orig = 0.010884, loss_refine = 0.936813[Step 384] loss_orig = 0.002007, loss_refine = -0.933190 [Step 384] loss_orig = 0.001501, loss_refine = -0.932824 [Step 384] loss_orig = 0.002587, loss_refine = 0.936948 [Step 384] loss_orig = 0.001901, loss_refine = 0.937284 Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 830223: cache has only 0 modules 32%|███▏ | 385/1208 [5:11:52<12:30:04, 54.68s/it] {'loss': 0.0015, 'grad_norm': 28.92174592681191, 'learning_rate': 6.812913907284768e-07, 'completion_length': 109.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.2960252861181895, 'kl': 0.05181884765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.55} 32%|███▏ | 385/1208 [5:11:52<12:30:04, 54.68s/it]Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 831096: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 831969: cache has only 0 modules 32%|███▏ | 386/1208 [5:12:28<11:11:08, 48.99s/it] {'loss': 0.0017, 'grad_norm': 5.507702169302893, 'learning_rate': 6.804635761589403e-07, 'completion_length': 84.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0433349609375, 'clip_ratio': 0.0, 'epoch': 2.56} 32%|███▏ | 386/1208 [5:12:28<11:11:08, 48.99s/it]Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 832842: cache has only 0 modules Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 833715: cache has only 0 modules 32%|███▏ | 387/1208 [5:13:08<10:33:44, 46.32s/it] {'loss': 0.002, 'grad_norm': 8.451387822328654, 'learning_rate': 6.796357615894039e-07, 'completion_length': 104.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.04931640625, 'clip_ratio': 0.0, 'epoch': 2.56} 32%|███▏ | 387/1208 [5:13:08<10:33:44, 46.32s/it]Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 834588: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 835461: cache has only 0 modules 32%|███▏ | 388/1208 [5:13:47<10:02:12, 44.06s/it] {'loss': 0.0019, 'grad_norm': 8.312616935567183, 'learning_rate': 6.788079470198676e-07, 'completion_length': 100.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0472412109375, 'clip_ratio': 0.0, 'epoch': 2.57} 32%|███▏ | 388/1208 [5:13:47<10:02:12, 44.06s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 836334: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 837207: cache has only 0 modules 32%|███▏ | 389/1208 [5:14:28<9:49:35, 43.19s/it] {'loss': 0.0024, 'grad_norm': 15.457360154274733, 'learning_rate': 6.779801324503311e-07, 'completion_length': 96.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.060791015625, 'clip_ratio': 0.0, 'epoch': 2.58} 32%|███▏ | 389/1208 [5:14:28<9:49:35, 43.19s/it]Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 838080: cache has only 0 modules Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 838953: cache has only 0 modules 32%|███▏ | 390/1208 [5:15:11<9:45:59, 42.98s/it] {'loss': 0.0018, 'grad_norm': 18.46547892681912, 'learning_rate': 6.771523178807946e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.045166015625, 'clip_ratio': 0.0, 'epoch': 2.58} 32%|███▏ | 390/1208 [5:15:11<9:45:59, 42.98s/it]Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 839826: cache has only 0 modules Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 840699: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'choose watercolor brush style'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 841572: cache has only 0 modules [Step 390] loss_orig = 0.001620, loss_refine = -1.059064[Step 390] loss_orig = 0.001115, loss_refine = 0.354962[Step 390] loss_orig = 0.002611, loss_refine = 0.354912 [Step 390] loss_orig = 0.001305, loss_refine = 0.355560 [Step 390] loss_orig = 0.001558, loss_refine = -1.059314 [Step 390] loss_orig = 0.001025, loss_refine = -1.059385 [Step 390] loss_orig = 0.004758, loss_refine = 1.770421 [Step 390] loss_orig = 0.001342, loss_refine = 0.354580 32%|███▏ | 391/1208 [5:16:08<10:42:26, 47.18s/it] {'loss': 0.0029, 'grad_norm': 9.176062636190824, 'learning_rate': 6.763245033112583e-07, 'completion_length': 98.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.23570225636164346, 'kl': 0.0780029296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 2.59} 32%|███▏ | 391/1208 [5:16:08<10:42:26, 47.18s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 842445: cache has only 0 modules Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 843318: cache has only 0 modules 32%|███▏ | 392/1208 [5:16:49<10:16:41, 45.35s/it] {'loss': 0.0016, 'grad_norm': 11.646035912513444, 'learning_rate': 6.754966887417219e-07, 'completion_length': 99.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.03936767578125, 'clip_ratio': 0.0, 'epoch': 2.6} 32%|███▏ | 392/1208 [5:16:49<10:16:41, 45.35s/it]Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 844191: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 845064: cache has only 0 modules 33%|███▎ | 393/1208 [5:17:33<10:11:24, 45.01s/it] {'loss': 0.0017, 'grad_norm': 9.242842252792755, 'learning_rate': 6.746688741721854e-07, 'completion_length': 106.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0418701171875, 'clip_ratio': 0.0, 'epoch': 2.6} 33%|███▎ | 393/1208 [5:17:33<10:11:24, 45.01s/it]Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 845937: cache has only 0 modules Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 846810: cache has only 0 modules 33%|███▎ | 394/1208 [5:18:12<9:45:16, 43.14s/it] {'loss': 0.0026, 'grad_norm': 5.678871206367529, 'learning_rate': 6.73841059602649e-07, 'completion_length': 109.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.064208984375, 'clip_ratio': 0.0, 'epoch': 2.61} 33%|███▎ | 394/1208 [5:18:12<9:45:16, 43.14s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 847683: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 848556: cache has only 0 modules 33%|███▎ | 395/1208 [5:18:44<8:58:17, 39.73s/it] {'loss': 0.0012, 'grad_norm': 0.29458249017685006, 'learning_rate': 6.730132450331126e-07, 'completion_length': 82.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03082275390625, 'clip_ratio': 0.0, 'epoch': 2.62} 33%|███▎ | 395/1208 [5:18:44<8:58:17, 39.73s/it]Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 849429: cache has only 0 modules Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 850302: cache has only 0 modules 33%|███▎ | 396/1208 [5:19:26<9:06:50, 40.41s/it] {'loss': 0.002, 'grad_norm': 22.76807042631264, 'learning_rate': 6.721854304635761e-07, 'completion_length': 100.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.04888916015625, 'clip_ratio': 0.0, 'epoch': 2.62} 33%|███▎ | 396/1208 [5:19:26<9:06:50, 40.41s/it]Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 851175: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 852048: cache has only 0 modules 33%|███▎ | 397/1208 [5:20:14<9:40:07, 42.92s/it] {'loss': 0.002, 'grad_norm': 12.391663457030258, 'learning_rate': 6.713576158940397e-07, 'completion_length': 109.1875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.7071067541837692, 'kl': 0.0489501953125, 'clip_ratio': 0.0, 'epoch': 2.63} 33%|███▎ | 397/1208 [5:20:14<9:40:07, 42.92s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 852921: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 853794: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: topic-link-a151002'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 854667: cache has only 0 modules [Step 397] loss_orig = 0.001105, loss_refine = -0.723497[Step 397] loss_orig = 0.001015, loss_refine = -0.723135 [Step 397] loss_orig = 0.003453, loss_refine = -0.723800 [Step 397] loss_orig = 0.002607, loss_refine = -0.723771[Step 397] loss_orig = 0.001479, loss_refine = 1.208603 [Step 397] loss_orig = 0.002868, loss_refine = 1.208608 [Step 397] loss_orig = 0.001734, loss_refine = -0.722856 [Step 397] loss_orig = 0.001419, loss_refine = 1.208889 33%|███▎ | 398/1208 [5:21:08<10:23:10, 46.16s/it] {'loss': 0.0013, 'grad_norm': 5.687068885927798, 'learning_rate': 6.705298013245033e-07, 'completion_length': 101.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0423583984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 2.64} 33%|███▎ | 398/1208 [5:21:08<10:23:10, 46.16s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 855540: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 856413: cache has only 0 modules 33%|███▎ | 399/1208 [5:21:46<9:50:16, 43.78s/it] {'loss': 0.0034, 'grad_norm': 6.790433795939932, 'learning_rate': 6.697019867549668e-07, 'completion_length': 92.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.084716796875, 'clip_ratio': 0.0, 'epoch': 2.64} 33%|███▎ | 399/1208 [5:21:46<9:50:16, 43.78s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 857286: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 858159: cache has only 0 modules 33%|███▎ | 400/1208 [5:22:27<9:37:45, 42.90s/it] {'loss': 0.002, 'grad_norm': 1.1663025119833204, 'learning_rate': 6.688741721854304e-07, 'completion_length': 91.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0496826171875, 'clip_ratio': 0.0, 'epoch': 2.65} 33%|███▎ | 400/1208 [5:22:27<9:37:45, 42.90s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 859032: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 859905: cache has only 0 modules 33%|███▎ | 401/1208 [5:23:04<9:14:02, 41.19s/it] {'loss': 0.0017, 'grad_norm': 0.3397073974905749, 'learning_rate': 6.68046357615894e-07, 'completion_length': 102.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0430908203125, 'clip_ratio': 0.0, 'epoch': 2.66} 33%|███▎ | 401/1208 [5:23:04<9:14:02, 41.19s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 860778: cache has only 0 modules Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 861651: cache has only 0 modules 33%|███▎ | 402/1208 [5:23:42<9:00:52, 40.26s/it] {'loss': 0.0015, 'grad_norm': 5.229458435866122, 'learning_rate': 6.672185430463576e-07, 'completion_length': 102.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0367431640625, 'clip_ratio': 0.0, 'epoch': 2.66} 33%|███▎ | 402/1208 [5:23:42<9:00:52, 40.26s/it]Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 862524: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Font Name'. Your previous answer was [{'action': 'click', 'coordinate': [320, 145] }] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 863397: cache has only 0 modules [Step 402] loss_orig = 0.001690, loss_refine = -0.148770[Step 402] loss_orig = 0.001380, loss_refine = -1.345506 [Step 402] loss_orig = 0.001179, loss_refine = -1.346262 [Step 402] loss_orig = 0.001438, loss_refine = -0.148497[Step 402] loss_orig = 0.001588, loss_refine = 1.049121[Step 402] loss_orig = 0.000842, loss_refine = 1.049841 [Step 402] loss_orig = 0.000837, loss_refine = -0.147706 [Step 402] loss_orig = 0.001322, loss_refine = 1.049514 Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 864270: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 865143: cache has only 0 modules [Step 402] loss_orig = 0.001976, loss_refine = -1.078072 [Step 402] loss_orig = 0.001073, loss_refine = 0.001167[Step 402] loss_orig = 0.002570, loss_refine = 0.002642 [Step 402] loss_orig = 0.001424, loss_refine = 0.001198 [Step 402] loss_orig = 0.003246, loss_refine = 0.002812 [Step 402] loss_orig = 0.001173, loss_refine = 0.001745 [Step 402] loss_orig = 0.003260, loss_refine = -1.078668 [Step 402] loss_orig = 0.001597, loss_refine = 2.161616 33%|███▎ | 403/1208 [5:25:11<12:14:57, 54.78s/it] {'loss': 0.0016, 'grad_norm': 8.767808795717412, 'learning_rate': 6.663907284768212e-07, 'completion_length': 126.375, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 0.96875, 'reward': 2.21875, 'reward_std': 0.440085768699646, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 2.67} 33%|███▎ | 403/1208 [5:25:11<12:14:57, 54.78s/it]Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 866016: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 866889: cache has only 0 modules 33%|███▎ | 404/1208 [5:25:55<11:30:09, 51.50s/it] {'loss': 0.002, 'grad_norm': 5.9865539931612854, 'learning_rate': 6.655629139072847e-07, 'completion_length': 102.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4629100561141968, 'kl': 0.050048828125, 'clip_ratio': 0.0, 'epoch': 2.68} 33%|███▎ | 404/1208 [5:25:55<11:30:09, 51.50s/it]Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 867762: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 868635: cache has only 0 modules [Step 404] loss_orig = 0.000893, loss_refine = -1.204043 [Step 404] loss_orig = 0.002033, loss_refine = 0.726357 [Step 404] loss_orig = 0.003864, loss_refine = -1.205231 [Step 404] loss_orig = 0.002027, loss_refine = -1.200640 [Step 404] loss_orig = 0.004552, loss_refine = 0.726136 [Step 404] loss_orig = 0.001876, loss_refine = 0.727470 [Step 404] loss_orig = 0.002042, loss_refine = 0.726596 [Step 404] loss_orig = 0.001717, loss_refine = 0.726709 Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 869508: cache has only 0 modules 34%|███▎ | 405/1208 [5:27:00<12:22:26, 55.47s/it] {'loss': 0.0041, 'grad_norm': 5.052393239925191, 'learning_rate': 6.647350993377483e-07, 'completion_length': 103.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.0955810546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.68} 34%|███▎ | 405/1208 [5:27:00<12:22:26, 55.47s/it]Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 870381: cache has only 0 modules Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 871254: cache has only 0 modules 34%|███▎ | 406/1208 [5:27:47<11:47:36, 52.94s/it] {'loss': 0.0025, 'grad_norm': 8.514903851792976, 'learning_rate': 6.639072847682119e-07, 'completion_length': 110.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.3535533845424652, 'kl': 0.0634765625, 'clip_ratio': 0.0, 'epoch': 2.69} 34%|███▎ | 406/1208 [5:27:47<11:47:36, 52.94s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 872127: cache has only 0 modules Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 873000: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'add alarm to the included controls'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 873873: cache has only 0 modules [Step 406] loss_orig = 0.001848, loss_refine = -0.539070 [Step 406] loss_orig = 0.002184, loss_refine = 1.620750 [Step 406] loss_orig = 0.001062, loss_refine = 1.624934 [Step 406] loss_orig = 0.004384, loss_refine = -0.539020 [Step 406] loss_orig = 0.001654, loss_refine = -0.537975 [Step 406] loss_orig = 0.001032, loss_refine = -0.539344[Step 406] loss_orig = 0.006509, loss_refine = -0.539009 [Step 406] loss_orig = 0.000850, loss_refine = -0.539346 34%|███▎ | 407/1208 [5:28:43<12:02:08, 54.09s/it] {'loss': 0.0012, 'grad_norm': 18.48445171379889, 'learning_rate': 6.630794701986755e-07, 'completion_length': 92.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.04278564453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 2.7} 34%|███▎ | 407/1208 [5:28:43<12:02:08, 54.09s/it]Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 874746: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 875619: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. Your previous answer was [{'action': 'click', 'coordinate': [1797, 140]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 876492: cache has only 0 modules [Step 407] loss_orig = -0.350073, loss_refine = 0.540926 [Step 407] loss_orig = -0.348978, loss_refine = 0.541582 [Step 407] loss_orig = -0.351132, loss_refine = 0.543386 [Step 407] loss_orig = -0.350887, loss_refine = -1.618323 [Step 407] loss_orig = -0.351228, loss_refine = -1.618207 [Step 407] loss_orig = 2.475517, loss_refine = 0.541651 [Step 407] loss_orig = -0.352240, loss_refine = 0.546833 [Step 407] loss_orig = -0.347144, loss_refine = 0.541003 34%|███▍ | 408/1208 [5:30:04<13:46:55, 62.02s/it] {'loss': 0.0022, 'grad_norm': 12.978999182614956, 'learning_rate': 6.622516556291391e-07, 'completion_length': 111.58333333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.1666666666666665, 'reward_std': 0.756801575422287, 'kl': 0.06201171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 2.7} 34%|███▍ | 408/1208 [5:30:04<13:46:55, 62.02s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 877365: cache has only 0 modules Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 878238: cache has only 0 modules 34%|███▍ | 409/1208 [5:30:50<12:41:13, 57.16s/it] {'loss': 0.0018, 'grad_norm': 10.862738576894406, 'learning_rate': 6.614238410596025e-07, 'completion_length': 111.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.045654296875, 'clip_ratio': 0.0, 'epoch': 2.71} 34%|███▍ | 409/1208 [5:30:50<12:41:13, 57.16s/it]Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 879111: cache has only 0 modules Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 879984: cache has only 0 modules 34%|███▍ | 410/1208 [5:31:30<11:32:31, 52.07s/it] {'loss': 0.0015, 'grad_norm': 3.6697209463859544, 'learning_rate': 6.605960264900662e-07, 'completion_length': 97.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0362548828125, 'clip_ratio': 0.0, 'epoch': 2.72} 34%|███▍ | 410/1208 [5:31:30<11:32:31, 52.07s/it]Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 880857: cache has only 0 modules Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 881730: cache has only 0 modules 34%|███▍ | 411/1208 [5:32:06<10:29:33, 47.39s/it] {'loss': 0.0009, 'grad_norm': 5.5360945148945335, 'learning_rate': 6.597682119205298e-07, 'completion_length': 92.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0218505859375, 'clip_ratio': 0.0, 'epoch': 2.72} 34%|███▍ | 411/1208 [5:32:06<10:29:33, 47.39s/it]Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 882603: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 883476: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 884349: cache has only 0 modules [Step 411] loss_orig = 0.001734, loss_refine = 0.726815[Step 411] loss_orig = 0.002566, loss_refine = -1.206249 [Step 411] loss_orig = 0.001026, loss_refine = 0.725807 [Step 411] loss_orig = 0.001187, loss_refine = 0.725692 [Step 411] loss_orig = 0.001468, loss_refine = -1.206289[Step 411] loss_orig = 0.004947, loss_refine = 0.726009 [Step 411] loss_orig = 0.001679, loss_refine = -1.204994[Step 411] loss_orig = 0.001350, loss_refine = 0.725461 34%|███▍ | 412/1208 [5:33:06<11:16:11, 50.97s/it] {'loss': 0.002, 'grad_norm': 14.008103933600646, 'learning_rate': 6.589403973509934e-07, 'completion_length': 99.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.0546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.73} 34%|███▍ | 412/1208 [5:33:06<11:16:11, 50.97s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 885222: cache has only 0 modules Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 886095: cache has only 0 modules 34%|███▍ | 413/1208 [5:33:44<10:25:45, 47.23s/it] {'loss': 0.0017, 'grad_norm': 4.883758655960657, 'learning_rate': 6.581125827814568e-07, 'completion_length': 91.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.04144287109375, 'clip_ratio': 0.0, 'epoch': 2.74} 34%|███▍ | 413/1208 [5:33:44<10:25:45, 47.23s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 886968: cache has only 0 modules Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 887841: cache has only 0 modules 34%|███▍ | 414/1208 [5:34:18<9:30:04, 43.08s/it] {'loss': 0.0021, 'grad_norm': 6.505625311732231, 'learning_rate': 6.572847682119205e-07, 'completion_length': 99.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0518798828125, 'clip_ratio': 0.0, 'epoch': 2.74} 34%|███▍ | 414/1208 [5:34:18<9:30:04, 43.08s/it]Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 888714: cache has only 0 modules Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 889587: cache has only 0 modules 34%|███▍ | 415/1208 [5:34:56<9:10:01, 41.62s/it] {'loss': 0.0014, 'grad_norm': 9.196693235064245, 'learning_rate': 6.564569536423841e-07, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.035400390625, 'clip_ratio': 0.0, 'epoch': 2.75} 34%|███▍ | 415/1208 [5:34:56<9:10:01, 41.62s/it]Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 890460: cache has only 0 modules Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 891333: cache has only 0 modules 34%|███▍ | 416/1208 [5:35:33<8:51:37, 40.27s/it] {'loss': 0.0017, 'grad_norm': 4.357250351276521, 'learning_rate': 6.556291390728476e-07, 'completion_length': 86.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04156494140625, 'clip_ratio': 0.0, 'epoch': 2.75} 34%|███▍ | 416/1208 [5:35:33<8:51:37, 40.27s/it]Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 892206: cache has only 0 modules Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 893079: cache has only 0 modules 35%|███▍ | 417/1208 [5:36:12<8:44:09, 39.76s/it] {'loss': 0.0041, 'grad_norm': 6.999719350932967, 'learning_rate': 6.548013245033113e-07, 'completion_length': 87.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.10205078125, 'clip_ratio': 0.0, 'epoch': 2.76} 35%|███▍ | 417/1208 [5:36:12<8:44:09, 39.76s/it]Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 893952: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 894825: cache has only 0 modules 35%|███▍ | 418/1208 [5:36:52<8:46:07, 39.96s/it] {'loss': 0.0025, 'grad_norm': 5.566742625596784, 'learning_rate': 6.539735099337748e-07, 'completion_length': 97.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.06298828125, 'clip_ratio': 0.0, 'epoch': 2.77} 35%|███▍ | 418/1208 [5:36:52<8:46:07, 39.96s/it]Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 895698: cache has only 0 modules Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 896571: cache has only 0 modules 35%|███▍ | 419/1208 [5:37:20<7:59:33, 36.47s/it] {'loss': 0.0013, 'grad_norm': 7.006075049863885, 'learning_rate': 6.531456953642384e-07, 'completion_length': 71.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0328369140625, 'clip_ratio': 0.0, 'epoch': 2.77} 35%|███▍ | 419/1208 [5:37:20<7:59:33, 36.47s/it]Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 897444: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 898317: cache has only 0 modules [Step 419] loss_orig = 0.001320, loss_refine = -0.933430 [Step 419] loss_orig = 0.001358, loss_refine = 0.935938 [Step 419] loss_orig = 0.001892, loss_refine = -0.934163 [Step 419] loss_orig = 0.001138, loss_refine = 0.938046[Step 419] loss_orig = 0.000937, loss_refine = 0.936406 [Step 419] loss_orig = 0.000912, loss_refine = 0.936398 [Step 419] loss_orig = 0.000959, loss_refine = -0.933922 [Step 419] loss_orig = 0.001245, loss_refine = -0.933898 Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 899190: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open photo'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 900063: cache has only 0 modules [Step 419] loss_orig = -0.352574, loss_refine = -1.408763[Step 419] loss_orig = -0.352416, loss_refine = 0.846653[Step 419] loss_orig = -0.352513, loss_refine = 0.848810 [Step 419] loss_orig = -0.352174, loss_refine = -1.407862 [Step 419] loss_orig = -0.352556, loss_refine = -0.280616[Step 419] loss_orig = -0.352483, loss_refine = 0.848322 [Step 419] loss_orig = -0.351590, loss_refine = -0.280391 [Step 419] loss_orig = 2.476205, loss_refine = 0.847516 35%|███▍ | 420/1208 [5:38:38<10:39:28, 48.69s/it] {'loss': 0.0016, 'grad_norm': 21.855012972085284, 'learning_rate': 6.523178807947019e-07, 'completion_length': 91.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 0.96875, 'reward': 2.28125, 'reward_std': 0.4436202719807625, 'kl': 0.03070068359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 2.78} 35%|███▍ | 420/1208 [5:38:38<10:39:28, 48.69s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 900936: cache has only 0 modules Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 901809: cache has only 0 modules 35%|███▍ | 421/1208 [5:39:18<10:08:12, 46.37s/it] {'loss': 0.0013, 'grad_norm': 0.2904443818610108, 'learning_rate': 6.514900662251656e-07, 'completion_length': 89.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03179931640625, 'clip_ratio': 0.0, 'epoch': 2.79} 35%|███▍ | 421/1208 [5:39:19<10:08:12, 46.37s/it]Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 902682: cache has only 0 modules Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 903555: cache has only 0 modules 35%|███▍ | 422/1208 [5:39:52<9:15:47, 42.43s/it] {'loss': 0.0008, 'grad_norm': 3.7667091250197107, 'learning_rate': 6.506622516556292e-07, 'completion_length': 86.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02117919921875, 'clip_ratio': 0.0, 'epoch': 2.79} 35%|███▍ | 422/1208 [5:39:52<9:15:47, 42.43s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 904428: cache has only 0 modules Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 905301: cache has only 0 modules 35%|███▌ | 423/1208 [5:40:28<8:51:56, 40.66s/it] {'loss': 0.0015, 'grad_norm': 34.97261434526776, 'learning_rate': 6.498344370860926e-07, 'completion_length': 85.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'epoch': 2.8} 35%|███▌ | 423/1208 [5:40:28<8:51:56, 40.66s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 906174: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 907047: cache has only 0 modules [Step 423] loss_orig = 0.001853, loss_refine = -0.538178[Step 423] loss_orig = 0.001212, loss_refine = -0.537515 [Step 423] loss_orig = 0.001453, loss_refine = -0.538822[Step 423] loss_orig = 0.002576, loss_refine = -0.538696 [Step 423] loss_orig = 0.001025, loss_refine = -0.536849[Step 423] loss_orig = 0.001158, loss_refine = 1.622778[Step 423] loss_orig = 0.001055, loss_refine = -0.538283 [Step 423] loss_orig = 0.001903, loss_refine = 1.621020 Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 907920: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open gmail'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 908793: cache has only 0 modules [Step 423] loss_orig = 0.001856, loss_refine = -0.121678 [Step 423] loss_orig = 0.001874, loss_refine = -0.124366[Step 423] loss_orig = 0.001696, loss_refine = -0.124512[Step 423] loss_orig = 0.001910, loss_refine = -1.133497 [Step 423] loss_orig = 0.002704, loss_refine = -1.133204[Step 423] loss_orig = 0.003611, loss_refine = 0.889199 [Step 423] loss_orig = 0.003521, loss_refine = 1.897889 [Step 423] loss_orig = 0.005953, loss_refine = -0.124449 35%|███▌ | 424/1208 [5:41:48<11:25:18, 52.45s/it] {'loss': 0.0026, 'grad_norm': 35.22916243829312, 'learning_rate': 6.490066225165562e-07, 'completion_length': 103.09375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.0625, 'rewards/format_reward': 1.0, 'reward': 2.40625, 'reward_std': 0.36348532140254974, 'kl': 0.0552978515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.8125, 'epoch': 2.81} 35%|███▌ | 424/1208 [5:41:48<11:25:18, 52.45s/it]Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 909666: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display ip address'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 910539: cache has only 0 modules [Step 424] loss_orig = 0.019053, loss_refine = 0.003420[Step 424] loss_orig = 0.002937, loss_refine = 0.004289[Step 424] loss_orig = 0.005329, loss_refine = 0.000956 [Step 424] loss_orig = 0.001896, loss_refine = 0.003780 [Step 424] loss_orig = 0.002657, loss_refine = 0.008465[Step 424] loss_orig = 0.012946, loss_refine = 0.004034 [Step 424] loss_orig = 0.002290, loss_refine = 0.004129 [Step 424] loss_orig = 0.004931, loss_refine = 0.004680 Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 911412: cache has only 0 modules 35%|███▌ | 425/1208 [5:42:40<11:23:13, 52.35s/it] {'loss': 0.003, 'grad_norm': 7.387575815005999, 'learning_rate': 6.481788079470199e-07, 'completion_length': 89.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.15430335203806558, 'kl': 0.1043701171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 2.81} 35%|███▌ | 425/1208 [5:42:40<11:23:13, 52.35s/it]Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 912285: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 913158: cache has only 0 modules 35%|███▌ | 426/1208 [5:43:15<10:13:46, 47.09s/it] {'loss': 0.0015, 'grad_norm': 0.4021566480369205, 'learning_rate': 6.473509933774834e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'epoch': 2.82} 35%|███▌ | 426/1208 [5:43:15<10:13:46, 47.09s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 914031: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 914904: cache has only 0 modules 35%|███▌ | 427/1208 [5:43:56<9:50:04, 45.33s/it] {'loss': 0.0017, 'grad_norm': 7.8853137253873085, 'learning_rate': 6.46523178807947e-07, 'completion_length': 85.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 2.83} 35%|███▌ | 427/1208 [5:43:56<9:50:04, 45.33s/it]Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 915777: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to song lyric'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 916650: cache has only 0 modules [Step 427] loss_orig = 0.001922, loss_refine = -1.206632 [Step 427] loss_orig = 0.001140, loss_refine = 0.725732[Step 427] loss_orig = 0.001430, loss_refine = 0.726546[Step 427] loss_orig = 0.004511, loss_refine = 0.725467 [Step 427] loss_orig = 0.001005, loss_refine = 0.726990[Step 427] loss_orig = 0.003070, loss_refine = -1.206404 [Step 427] loss_orig = 0.002173, loss_refine = 0.725783 [Step 427] loss_orig = 0.002725, loss_refine = -1.206559 Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 917523: cache has only 0 modules 35%|███▌ | 428/1208 [5:44:46<10:06:44, 46.67s/it] {'loss': 0.0015, 'grad_norm': 16.153620599736037, 'learning_rate': 6.456953642384105e-07, 'completion_length': 82.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.048095703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 2.83} 35%|███▌ | 428/1208 [5:44:46<10:06:44, 46.67s/it]Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 918396: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 919269: cache has only 0 modules 36%|███▌ | 429/1208 [5:45:24<9:32:51, 44.12s/it] {'loss': 0.0015, 'grad_norm': 5.1800270424486685, 'learning_rate': 6.448675496688742e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0384521484375, 'clip_ratio': 0.0, 'epoch': 2.84} 36%|███▌ | 429/1208 [5:45:24<9:32:51, 44.12s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 920142: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 921015: cache has only 0 modules 36%|███▌ | 430/1208 [5:45:58<8:53:14, 41.12s/it] {'loss': 0.0033, 'grad_norm': 14.705584992748337, 'learning_rate': 6.440397350993377e-07, 'completion_length': 77.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0830078125, 'clip_ratio': 0.0, 'epoch': 2.85} 36%|███▌ | 430/1208 [5:45:58<8:53:14, 41.12s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 921888: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 922761: cache has only 0 modules 36%|███▌ | 431/1208 [5:46:27<8:04:43, 37.43s/it] {'loss': 0.001, 'grad_norm': 0.21755740092618944, 'learning_rate': 6.432119205298013e-07, 'completion_length': 76.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02423095703125, 'clip_ratio': 0.0, 'epoch': 2.85} 36%|███▌ | 431/1208 [5:46:27<8:04:43, 37.43s/it]Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 923634: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 924507: cache has only 0 modules 36%|███▌ | 432/1208 [5:47:02<7:51:36, 36.46s/it] {'loss': 0.0014, 'grad_norm': 7.1864830931100885, 'learning_rate': 6.423841059602649e-07, 'completion_length': 74.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03521728515625, 'clip_ratio': 0.0, 'epoch': 2.86} 36%|███▌ | 432/1208 [5:47:02<7:51:36, 36.46s/it]Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 925380: cache has only 0 modules Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 926253: cache has only 0 modules 36%|███▌ | 433/1208 [5:47:32<7:28:08, 34.69s/it] {'loss': 0.0014, 'grad_norm': 10.734467275669656, 'learning_rate': 6.415562913907284e-07, 'completion_length': 88.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.035400390625, 'clip_ratio': 0.0, 'epoch': 2.87} 36%|███▌ | 433/1208 [5:47:32<7:28:08, 34.69s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 927126: cache has only 0 modules Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 927999: cache has only 0 modules 36%|███▌ | 434/1208 [5:48:07<7:28:28, 34.77s/it] {'loss': 0.0015, 'grad_norm': 3.4109262502036928, 'learning_rate': 6.40728476821192e-07, 'completion_length': 78.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'epoch': 2.87} 36%|███▌ | 434/1208 [5:48:07<7:28:28, 34.77s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 928872: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 929745: cache has only 0 modules 36%|███▌ | 435/1208 [5:48:54<8:16:01, 38.50s/it] {'loss': 0.0028, 'grad_norm': 4.030811663093513, 'learning_rate': 6.399006622516556e-07, 'completion_length': 101.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.06884765625, 'clip_ratio': 0.0, 'epoch': 2.88} 36%|███▌ | 435/1208 [5:48:54<8:16:01, 38.50s/it]Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 930618: cache has only 0 modules Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 931491: cache has only 0 modules 36%|███▌ | 436/1208 [5:49:33<8:16:26, 38.58s/it] {'loss': 0.002, 'grad_norm': 5.965752641040892, 'learning_rate': 6.390728476821193e-07, 'completion_length': 86.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0491943359375, 'clip_ratio': 0.0, 'epoch': 2.89} 36%|███▌ | 436/1208 [5:49:33<8:16:26, 38.58s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 932364: cache has only 0 modules Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 933237: cache has only 0 modules 36%|███▌ | 437/1208 [5:50:14<8:26:19, 39.40s/it] {'loss': 0.0013, 'grad_norm': 18.64476209908518, 'learning_rate': 6.382450331125827e-07, 'completion_length': 95.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'epoch': 2.89} 36%|███▌ | 437/1208 [5:50:14<8:26:19, 39.40s/it]Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 934110: cache has only 0 modules Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 934983: cache has only 0 modules 36%|███▋ | 438/1208 [5:50:50<8:12:46, 38.40s/it] {'loss': 0.0016, 'grad_norm': 3.5069417083467482, 'learning_rate': 6.374172185430463e-07, 'completion_length': 90.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0389404296875, 'clip_ratio': 0.0, 'epoch': 2.9} 36%|███▋ | 438/1208 [5:50:50<8:12:46, 38.40s/it]Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 935856: cache has only 0 modules Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 936729: cache has only 0 modules 36%|███▋ | 439/1208 [5:51:26<8:00:00, 37.45s/it] {'loss': 0.0011, 'grad_norm': 0.20754773311220956, 'learning_rate': 6.365894039735099e-07, 'completion_length': 96.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02728271484375, 'clip_ratio': 0.0, 'epoch': 2.91} 36%|███▋ | 439/1208 [5:51:26<8:00:00, 37.45s/it]Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 937602: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 938475: cache has only 0 modules [Step 439] loss_orig = 0.002534, loss_refine = 0.003826 [Step 439] loss_orig = 0.002301, loss_refine = 0.000907 [Step 439] loss_orig = 0.001362, loss_refine = 0.001278[Step 439] loss_orig = 0.004377, loss_refine = 0.001612 [Step 439] loss_orig = 0.002424, loss_refine = 0.001671[Step 439] loss_orig = 0.002564, loss_refine = 0.000773 [Step 439] loss_orig = 0.003287, loss_refine = 0.001029 [Step 439] loss_orig = 0.002078, loss_refine = 0.001516 Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 939348: cache has only 0 modules 36%|███▋ | 440/1208 [5:52:19<8:59:04, 42.11s/it] {'loss': 0.0017, 'grad_norm': 7.6315549134525495, 'learning_rate': 6.357615894039735e-07, 'completion_length': 89.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.0833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.055908203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 2.91} 36%|███▋ | 440/1208 [5:52:19<8:59:04, 42.11s/it]Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 940221: cache has only 0 modules Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 941094: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 941967: cache has only 0 modules [Step 440] loss_orig = 0.002314, loss_refine = 0.001407[Step 440] loss_orig = 0.001770, loss_refine = 0.002738[Step 440] loss_orig = 0.001872, loss_refine = 0.001526 [Step 440] loss_orig = 0.003532, loss_refine = 0.003536[Step 440] loss_orig = 0.001672, loss_refine = 0.001142 [Step 440] loss_orig = 0.002431, loss_refine = 0.001112 [Step 440] loss_orig = 0.002612, loss_refine = 0.001510 [Step 440] loss_orig = 0.001751, loss_refine = 0.000652 37%|███▋ | 441/1208 [5:53:10<9:32:30, 44.79s/it] {'loss': 0.0013, 'grad_norm': 19.52635689087563, 'learning_rate': 6.349337748344371e-07, 'completion_length': 88.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.0833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.04034423828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 2.92} 37%|███▋ | 441/1208 [5:53:10<9:32:30, 44.79s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 942840: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 943713: cache has only 0 modules 37%|███▋ | 442/1208 [5:53:52<9:22:25, 44.05s/it] {'loss': 0.0023, 'grad_norm': 6.6548233155494785, 'learning_rate': 6.341059602649006e-07, 'completion_length': 92.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.056640625, 'clip_ratio': 0.0, 'epoch': 2.93} 37%|███▋ | 442/1208 [5:53:52<9:22:25, 44.05s/it]Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 944586: cache has only 0 modules Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 945459: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 946332: cache has only 0 modules [Step 442] loss_orig = 0.001790, loss_refine = 0.001023[Step 442] loss_orig = 0.001218, loss_refine = 0.002018 [Step 442] loss_orig = 0.005689, loss_refine = 0.001752[Step 442] loss_orig = 0.003986, loss_refine = 1.871181 [Step 442] loss_orig = 0.002755, loss_refine = 0.001163 [Step 442] loss_orig = 0.001133, loss_refine = 0.000781[Step 442] loss_orig = 0.001763, loss_refine = 0.002506 [Step 442] loss_orig = 0.001353, loss_refine = -1.867981 37%|███▋ | 443/1208 [5:54:45<9:55:29, 46.71s/it] {'loss': 0.0014, 'grad_norm': 10.580977576831968, 'learning_rate': 6.332781456953642e-07, 'completion_length': 97.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.2960252861181895, 'kl': 0.045166015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 2.93} 37%|███▋ | 443/1208 [5:54:45<9:55:29, 46.71s/it]Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 947205: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 948078: cache has only 0 modules 37%|███▋ | 444/1208 [5:55:29<9:45:52, 46.01s/it] {'loss': 0.002, 'grad_norm': 5.41089381199249, 'learning_rate': 6.324503311258278e-07, 'completion_length': 102.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.05029296875, 'clip_ratio': 0.0, 'epoch': 2.94} 37%|███▋ | 444/1208 [5:55:29<9:45:52, 46.01s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 948951: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 949824: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_ArrowCircle_M'. Your previous answer was [{'action': 'click', 'coordinate': [332, 907]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 950697: cache has only 0 modules [Step 444] loss_orig = 0.001521, loss_refine = 0.662875 [Step 444] loss_orig = 0.001453, loss_refine = -0.659342 [Step 444] loss_orig = 0.001901, loss_refine = 0.662510 [Step 444] loss_orig = 0.000928, loss_refine = 0.662050[Step 444] loss_orig = 0.002270, loss_refine = 0.663351 [Step 444] loss_orig = 0.002862, loss_refine = -0.659966 [Step 444] loss_orig = 0.002409, loss_refine = 0.662345 [Step 444] loss_orig = 0.001815, loss_refine = -1.982430 37%|███▋ | 445/1208 [5:56:29<10:36:23, 50.04s/it] {'loss': 0.0015, 'grad_norm': 5.7323282148314805, 'learning_rate': 6.316225165562914e-07, 'completion_length': 98.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.2519763112068176, 'kl': 0.042236328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 2.95} 37%|███▋ | 445/1208 [5:56:29<10:36:23, 50.04s/it]Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 951570: cache has only 0 modules Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 952443: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Decorative Locked'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 953316: cache has only 0 modules [Step 445] loss_orig = 0.001385, loss_refine = -0.280109[Step 445] loss_orig = 0.001106, loss_refine = -0.279883[Step 445] loss_orig = 0.002141, loss_refine = -0.280022[Step 445] loss_orig = 0.001105, loss_refine = -1.408742 [Step 445] loss_orig = 0.001613, loss_refine = -0.279001 [Step 445] loss_orig = 0.001060, loss_refine = -0.280503 [Step 445] loss_orig = 0.002513, loss_refine = 0.847912 [Step 445] loss_orig = 0.001729, loss_refine = 1.977420 37%|███▋ | 446/1208 [5:57:34<11:32:14, 54.51s/it] {'loss': 0.003, 'grad_norm': 3.0561661148007477, 'learning_rate': 6.307947019867548e-07, 'completion_length': 109.79166666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.25, 'reward_std': 0.29546840985616046, 'kl': 0.067626953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 2.95} 37%|███▋ | 446/1208 [5:57:34<11:32:14, 54.51s/it]Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 954189: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 955062: cache has only 0 modules 37%|███▋ | 447/1208 [5:58:12<10:30:08, 49.68s/it] {'loss': 0.0058, 'grad_norm': 32.345020610664065, 'learning_rate': 6.299668874172185e-07, 'completion_length': 90.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.145263671875, 'clip_ratio': 0.0, 'epoch': 2.96} 37%|███▋ | 447/1208 [5:58:12<10:30:08, 49.68s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 955935: cache has only 0 modules Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 956808: cache has only 0 modules 37%|███▋ | 448/1208 [5:58:49<9:40:13, 45.81s/it] {'loss': 0.0019, 'grad_norm': 0.6055502312868682, 'learning_rate': 6.291390728476821e-07, 'completion_length': 93.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0472412109375, 'clip_ratio': 0.0, 'epoch': 2.97} 37%|███▋ | 448/1208 [5:58:49<9:40:13, 45.81s/it]Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 957681: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 958554: cache has only 0 modules 37%|███▋ | 449/1208 [5:59:29<9:16:37, 44.00s/it] {'loss': 0.0016, 'grad_norm': 0.29580251861073054, 'learning_rate': 6.283112582781457e-07, 'completion_length': 88.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.040283203125, 'clip_ratio': 0.0, 'epoch': 2.97} 37%|███▋ | 449/1208 [5:59:29<9:16:37, 44.00s/it]Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 959427: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 960300: cache has only 0 modules [Step 449] loss_orig = 0.003673, loss_refine = -0.535378 [Step 449] loss_orig = 0.001350, loss_refine = 1.620999 [Step 449] loss_orig = 0.002061, loss_refine = -0.536478[Step 449] loss_orig = 0.005060, loss_refine = -0.538154 [Step 449] loss_orig = 0.002999, loss_refine = -0.536179[Step 449] loss_orig = 0.002342, loss_refine = -0.536633 [Step 449] loss_orig = 0.002418, loss_refine = -0.538297[Step 449] loss_orig = 0.004391, loss_refine = 1.621101 Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 961173: cache has only 0 modules 37%|███▋ | 450/1208 [6:00:27<10:12:13, 48.46s/it] {'loss': 0.0022, 'grad_norm': 5.902497934188136, 'learning_rate': 6.274834437086092e-07, 'completion_length': 94.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.0599365234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 2.98} 37%|███▋ | 450/1208 [6:00:27<10:12:13, 48.46s/it]Start loss calc for inst: click the UI element Feedback /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 962046: cache has only 0 modules Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 962919: cache has only 0 modules 37%|███▋ | 451/1208 [6:01:15<10:07:43, 48.17s/it] {'loss': 0.0016, 'grad_norm': 0.4775293707791662, 'learning_rate': 6.266556291390728e-07, 'completion_length': 79.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04095458984375, 'clip_ratio': 0.0, 'epoch': 2.99} 37%|███▋ | 451/1208 [6:01:15<10:07:43, 48.17s/it]Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 963792: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 964665: cache has only 0 modules 37%|███▋ | 452/1208 [6:01:46<9:03:47, 43.16s/it] {'loss': 0.0015, 'grad_norm': 32.23744742838669, 'learning_rate': 6.258278145695364e-07, 'completion_length': 82.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0382080078125, 'clip_ratio': 0.0, 'epoch': 2.99} 37%|███▋ | 452/1208 [6:01:46<9:03:47, 43.16s/it]Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 965538: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 966411: cache has only 0 modules 38%|███▊ | 453/1208 [6:02:28<8:55:31, 42.56s/it] {'loss': 0.0019, 'grad_norm': 15.525218025171888, 'learning_rate': 6.249999999999999e-07, 'completion_length': 109.00000381469727, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.6307864785194397, 'kl': 0.04010009765625, 'clip_ratio': 0.0, 'epoch': 3.0} 38%|███▊ | 453/1208 [6:02:28<8:55:31, 42.56s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 967284: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 968157: cache has only 0 modules 38%|███▊ | 454/1208 [6:03:11<8:57:57, 42.81s/it] {'loss': 0.0028, 'grad_norm': 11.270372833270992, 'learning_rate': 6.241721854304636e-07, 'completion_length': 100.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.0712890625, 'clip_ratio': 0.0, 'epoch': 3.01} 38%|███▊ | 454/1208 [6:03:11<8:57:57, 42.81s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 969030: cache has only 0 modules Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 969903: cache has only 0 modules 38%|███▊ | 455/1208 [6:03:53<8:53:11, 42.48s/it] {'loss': 0.0023, 'grad_norm': 10.839379678341944, 'learning_rate': 6.233443708609272e-07, 'completion_length': 87.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.5850084125995636, 'kl': 0.0582275390625, 'clip_ratio': 0.0, 'epoch': 3.01} 38%|███▊ | 455/1208 [6:03:53<8:53:11, 42.48s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 970776: cache has only 0 modules Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 971649: cache has only 0 modules 38%|███▊ | 456/1208 [6:04:37<8:59:44, 43.06s/it] {'loss': 0.0024, 'grad_norm': 4.470459314935333, 'learning_rate': 6.225165562913907e-07, 'completion_length': 96.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.059326171875, 'clip_ratio': 0.0, 'epoch': 3.02} 38%|███▊ | 456/1208 [6:04:37<8:59:44, 43.06s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 972522: cache has only 0 modules Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 973395: cache has only 0 modules 38%|███▊ | 457/1208 [6:05:20<8:59:09, 43.08s/it] {'loss': 0.0015, 'grad_norm': 8.3074533973673, 'learning_rate': 6.216887417218542e-07, 'completion_length': 94.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.036376953125, 'clip_ratio': 0.0, 'epoch': 3.03} 38%|███▊ | 457/1208 [6:05:20<8:59:09, 43.08s/it]Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 974268: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 975141: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [825, 98]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 976014: cache has only 0 modules [Step 457] loss_orig = 0.001801, loss_refine = 1.050063[Step 457] loss_orig = 0.001900, loss_refine = 1.050144[Step 457] loss_orig = 0.001374, loss_refine = -0.148527[Step 457] loss_orig = 0.001100, loss_refine = -0.148014 [Step 457] loss_orig = 0.001978, loss_refine = 1.049973 [Step 457] loss_orig = 0.008030, loss_refine = -0.147679 [Step 457] loss_orig = 0.001087, loss_refine = -1.346755 [Step 457] loss_orig = 0.001792, loss_refine = -1.346725 38%|███▊ | 458/1208 [6:06:21<10:06:27, 48.52s/it] {'loss': 0.002, 'grad_norm': 35.56823100629541, 'learning_rate': 6.208609271523179e-07, 'completion_length': 108.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.4506907065709432, 'kl': 0.0599365234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.03} 38%|███▊ | 458/1208 [6:06:21<10:06:27, 48.52s/it]Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 976887: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 977760: cache has only 0 modules [Step 458] loss_orig = 0.001778, loss_refine = 0.000843[Step 458] loss_orig = 0.002091, loss_refine = 0.001977[Step 458] loss_orig = 0.002225, loss_refine = 0.000803 [Step 458] loss_orig = 0.003692, loss_refine = 0.001896[Step 458] loss_orig = 0.001560, loss_refine = 0.000988[Step 458] loss_orig = 0.001572, loss_refine = 0.002077 [Step 458] loss_orig = 0.003506, loss_refine = 0.000893 [Step 458] loss_orig = 0.002706, loss_refine = 0.001724 Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 978633: cache has only 0 modules 38%|███▊ | 459/1208 [6:07:12<10:12:21, 49.05s/it] {'loss': 0.0025, 'grad_norm': 8.78112219697182, 'learning_rate': 6.200331125827815e-07, 'completion_length': 86.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.11785112818082173, 'kl': 0.075927734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 3.04} 38%|███▊ | 459/1208 [6:07:12<10:12:21, 49.05s/it]Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 979506: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more settings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 980379: cache has only 0 modules [Step 459] loss_orig = 0.001590, loss_refine = -1.079243[Step 459] loss_orig = 0.004176, loss_refine = 1.082929 [Step 459] loss_orig = 0.001855, loss_refine = 1.082037 [Step 459] loss_orig = 0.004479, loss_refine = 0.001680 [Step 459] loss_orig = 0.001673, loss_refine = -1.078117 [Step 459] loss_orig = 0.003054, loss_refine = 1.081481 [Step 459] loss_orig = 0.002061, loss_refine = 0.002534[Step 459] loss_orig = 0.001621, loss_refine = -1.079141 Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 981252: cache has only 0 modules 38%|███▊ | 460/1208 [6:08:05<10:28:35, 50.42s/it] {'loss': 0.0018, 'grad_norm': 6.631170264694618, 'learning_rate': 6.19205298013245e-07, 'completion_length': 98.08333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5605830152829488, 'kl': 0.0557861328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.05} 38%|███▊ | 460/1208 [6:08:05<10:28:35, 50.42s/it]Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 982125: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open dynamic shot'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 982998: cache has only 0 modules [Step 460] loss_orig = 0.001666, loss_refine = -0.212820[Step 460] loss_orig = 0.001803, loss_refine = -0.212481 [Step 460] loss_orig = 0.001219, loss_refine = -0.213876 [Step 460] loss_orig = 0.000623, loss_refine = -1.071888 [Step 460] loss_orig = 0.000691, loss_refine = -0.213587[Step 460] loss_orig = 0.001492, loss_refine = -0.212986 [Step 460] loss_orig = 0.002013, loss_refine = -0.213232 [Step 460] loss_orig = 0.001417, loss_refine = 2.361402 Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 983871: cache has only 0 modules 38%|███▊ | 461/1208 [6:09:18<11:51:52, 57.18s/it] {'loss': 0.0017, 'grad_norm': 6.048198621948172, 'learning_rate': 6.183774834437085e-07, 'completion_length': 108.0, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.5416666666666665, 'reward_std': 0.5061726868152618, 'kl': 0.04296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 3.05} 38%|███▊ | 461/1208 [6:09:18<11:51:52, 57.18s/it]Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 984744: cache has only 0 modules Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 985617: cache has only 0 modules 38%|███▊ | 462/1208 [6:09:56<10:36:33, 51.20s/it] {'loss': 0.0011, 'grad_norm': 0.420683334285955, 'learning_rate': 6.175496688741722e-07, 'completion_length': 93.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0283203125, 'clip_ratio': 0.0, 'epoch': 3.06} 38%|███▊ | 462/1208 [6:09:56<10:36:33, 51.20s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 986490: cache has only 0 modules Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 987363: cache has only 0 modules 38%|███▊ | 463/1208 [6:10:34<9:47:16, 47.30s/it] {'loss': 0.0015, 'grad_norm': 5.0378720201254295, 'learning_rate': 6.167218543046358e-07, 'completion_length': 95.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03668212890625, 'clip_ratio': 0.0, 'epoch': 3.07} 38%|███▊ | 463/1208 [6:10:34<9:47:16, 47.30s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 988236: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 989109: cache has only 0 modules 38%|███▊ | 464/1208 [6:11:20<9:43:28, 47.05s/it] {'loss': 0.0019, 'grad_norm': 10.082800039257455, 'learning_rate': 6.158940397350993e-07, 'completion_length': 111.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0479736328125, 'clip_ratio': 0.0, 'epoch': 3.07} 38%|███▊ | 464/1208 [6:11:20<9:43:28, 47.05s/it]Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 989982: cache has only 0 modules Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 990855: cache has only 0 modules 38%|███▊ | 465/1208 [6:11:56<9:00:08, 43.62s/it] {'loss': 0.0013, 'grad_norm': 8.37833894901002, 'learning_rate': 6.150662251655628e-07, 'completion_length': 87.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03143310546875, 'clip_ratio': 0.0, 'epoch': 3.08} 38%|███▊ | 465/1208 [6:11:56<9:00:08, 43.62s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 991728: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 992601: cache has only 0 modules [Step 465] loss_orig = 0.001719, loss_refine = 0.355469[Step 465] loss_orig = 0.001373, loss_refine = 0.355990[Step 465] loss_orig = 0.000799, loss_refine = 0.354662 [Step 465] loss_orig = 0.000960, loss_refine = 0.354708 [Step 465] loss_orig = 0.001861, loss_refine = 0.354450 [Step 465] loss_orig = 0.002523, loss_refine = 0.354897 [Step 465] loss_orig = 0.001425, loss_refine = -2.473160 [Step 465] loss_orig = 0.001872, loss_refine = 0.354596 Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 993474: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Font Name'. Your previous answer was [{'action': 'click', 'coordinate': [278, 66]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 994347: cache has only 0 modules [Step 465] loss_orig = -0.352491, loss_refine = -0.537990[Step 465] loss_orig = -0.342268, loss_refine = -0.538335[Step 465] loss_orig = -0.352281, loss_refine = -0.538744[Step 465] loss_orig = -0.351492, loss_refine = -0.538922 [Step 465] loss_orig = -0.352539, loss_refine = -0.538755 [Step 465] loss_orig = -0.351809, loss_refine = 1.621405 [Step 465] loss_orig = 2.476094, loss_refine = 1.621051 [Step 465] loss_orig = -0.352295, loss_refine = -0.538637 39%|███▊ | 466/1208 [6:13:21<11:33:19, 56.06s/it] {'loss': 0.0014, 'grad_norm': 16.026381647792597, 'learning_rate': 6.142384105960265e-07, 'completion_length': 107.59375, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.96875, 'reward': 2.15625, 'reward_std': 0.3808925524353981, 'kl': 0.052001953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 3.09} 39%|███▊ | 466/1208 [6:13:21<11:33:19, 56.06s/it]Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 995220: cache has only 0 modules Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 996093: cache has only 0 modules 39%|███▊ | 467/1208 [6:14:05<10:46:58, 52.39s/it] {'loss': 0.0011, 'grad_norm': 0.16097503118162237, 'learning_rate': 6.1341059602649e-07, 'completion_length': 101.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02740478515625, 'clip_ratio': 0.0, 'epoch': 3.09} 39%|███▊ | 467/1208 [6:14:05<10:46:58, 52.39s/it]Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 996966: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 997839: cache has only 0 modules [Step 467] loss_orig = 0.001695, loss_refine = -0.193807[Step 467] loss_orig = 0.001041, loss_refine = -0.193813[Step 467] loss_orig = 0.000827, loss_refine = 1.365840 [Step 467] loss_orig = 0.001230, loss_refine = -0.193576 [Step 467] loss_orig = 0.001435, loss_refine = -1.752422 [Step 467] loss_orig = 0.000916, loss_refine = -0.193117 [Step 467] loss_orig = 0.000884, loss_refine = 1.366107 [Step 467] loss_orig = 0.001480, loss_refine = -0.194242 Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 998712: cache has only 0 modules 39%|███▊ | 468/1208 [6:14:56<10:43:07, 52.15s/it] {'loss': 0.0018, 'grad_norm': 5.491128887621497, 'learning_rate': 6.125827814569536e-07, 'completion_length': 88.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.21362332503000894, 'kl': 0.042236328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.1} 39%|███▊ | 468/1208 [6:14:56<10:43:07, 52.15s/it]Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 999585: cache has only 0 modules Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1000458: cache has only 0 modules 39%|███▉ | 469/1208 [6:15:38<10:04:00, 49.04s/it] {'loss': 0.0021, 'grad_norm': 12.13573312493132, 'learning_rate': 6.117549668874173e-07, 'completion_length': 103.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.052978515625, 'clip_ratio': 0.0, 'epoch': 3.11} 39%|███▉ | 469/1208 [6:15:38<10:04:00, 49.04s/it]Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1001331: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box diff coord reward error closer to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1002204: cache has only 0 modules [Step 469] loss_orig = 0.007036, loss_refine = -0.538979[Step 469] loss_orig = 0.001306, loss_refine = 1.620911 [Step 469] loss_orig = 0.002031, loss_refine = -0.538706 [Step 469] loss_orig = 0.001413, loss_refine = -0.538338 [Step 469] loss_orig = 0.000893, loss_refine = -0.538820 [Step 469] loss_orig = 0.001151, loss_refine = 1.621249 [Step 469] loss_orig = 0.001417, loss_refine = -0.538519 [Step 469] loss_orig = 0.001528, loss_refine = -0.538244 Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1003077: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1003950: cache has only 0 modules [Step 469] loss_orig = 0.002829, loss_refine = -0.537793 [Step 469] loss_orig = 0.001172, loss_refine = -0.536953 [Step 469] loss_orig = 0.002402, loss_refine = -0.534029[Step 469] loss_orig = 0.002367, loss_refine = 1.625968 [Step 469] loss_orig = 0.001303, loss_refine = -0.538052 [Step 469] loss_orig = 0.001693, loss_refine = -0.534312 [Step 469] loss_orig = 0.001447, loss_refine = -0.534509 [Step 469] loss_orig = 0.005248, loss_refine = 1.627873 39%|███▉ | 470/1208 [6:17:06<12:25:12, 60.59s/it] {'loss': 0.003, 'grad_norm': 8.263162969142583, 'learning_rate': 6.109271523178807e-07, 'completion_length': 114.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.2314550280570984, 'kl': 0.0550537109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 3.11} 39%|███▉ | 470/1208 [6:17:06<12:25:12, 60.59s/it]Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1004823: cache has only 0 modules Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1005696: cache has only 0 modules 39%|███▉ | 471/1208 [6:17:45<11:07:04, 54.31s/it] {'loss': 0.0017, 'grad_norm': 17.882656793382935, 'learning_rate': 6.100993377483443e-07, 'completion_length': 106.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4629100561141968, 'kl': 0.0433349609375, 'clip_ratio': 0.0, 'epoch': 3.12} 39%|███▉ | 471/1208 [6:17:45<11:07:04, 54.31s/it]Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1006569: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1007442: cache has only 0 modules 39%|███▉ | 472/1208 [6:18:27<10:19:37, 50.51s/it] {'loss': 0.0039, 'grad_norm': 8.487162016270556, 'learning_rate': 6.092715231788079e-07, 'completion_length': 104.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.097412109375, 'clip_ratio': 0.0, 'epoch': 3.13} 39%|███▉ | 472/1208 [6:18:27<10:19:37, 50.51s/it]Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1008315: cache has only 0 modules ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1009188: cache has only 0 modules [Step 472] loss_orig = 0.001601, loss_refine = 0.001552 [Step 472] loss_orig = 0.002039, loss_refine = 0.001212[Step 472] loss_orig = 0.001949, loss_refine = 0.002766 [Step 472] loss_orig = 0.003189, loss_refine = 0.002177 [Step 472] loss_orig = 0.001639, loss_refine = 0.001699[Step 472] loss_orig = 0.003054, loss_refine = 0.001560 [Step 472] loss_orig = 0.001574, loss_refine = 0.001645 [Step 472] loss_orig = 0.001623, loss_refine = 0.002482 Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1010061: cache has only 0 modules 39%|███▉ | 473/1208 [6:19:25<10:46:24, 52.77s/it] {'loss': 0.0014, 'grad_norm': 0.28574037121095097, 'learning_rate': 6.084437086092716e-07, 'completion_length': 101.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.03759765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.13} 39%|███▉ | 473/1208 [6:19:25<10:46:24, 52.77s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1010934: cache has only 0 modules Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1011807: cache has only 0 modules 39%|███▉ | 474/1208 [6:20:05<10:00:23, 49.08s/it] {'loss': 0.0015, 'grad_norm': 13.228080964673419, 'learning_rate': 6.076158940397351e-07, 'completion_length': 80.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.44403792917728424, 'kl': 0.03857421875, 'clip_ratio': 0.0, 'epoch': 3.14} 39%|███▉ | 474/1208 [6:20:05<10:00:23, 49.08s/it]Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1012680: cache has only 0 modules Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1013553: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'exchange target and source city'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1014426: cache has only 0 modules [Step 474] loss_orig = 0.001047, loss_refine = 0.725015[Step 474] loss_orig = 0.001191, loss_refine = -1.206671 [Step 474] loss_orig = 0.001030, loss_refine = 0.725082[Step 474] loss_orig = 0.000627, loss_refine = 0.724952 [Step 474] loss_orig = 0.000644, loss_refine = 0.725040[Step 474] loss_orig = 0.000472, loss_refine = 0.725469 [Step 474] loss_orig = 0.001138, loss_refine = -1.205452 [Step 474] loss_orig = 0.001235, loss_refine = -1.206591 39%|███▉ | 475/1208 [6:20:57<10:08:26, 49.80s/it] {'loss': 0.0008, 'grad_norm': 3.9975585296345115, 'learning_rate': 6.067880794701986e-07, 'completion_length': 87.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.17251638571421304, 'kl': 0.02044677734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 3.15} 39%|███▉ | 475/1208 [6:20:57<10:08:26, 49.80s/it]Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1015299: cache has only 0 modules Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1016172: cache has only 0 modules 39%|███▉ | 476/1208 [6:21:54<10:34:40, 52.02s/it] {'loss': 0.0037, 'grad_norm': 9.767719267945164, 'learning_rate': 6.059602649006622e-07, 'completion_length': 99.625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 0.9375, 'reward': 2.3125, 'reward_std': 0.6487165093421936, 'kl': 0.0926513671875, 'clip_ratio': 0.0, 'epoch': 3.15} 39%|███▉ | 476/1208 [6:21:54<10:34:40, 52.02s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1017045: cache has only 0 modules Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1017918: cache has only 0 modules 39%|███▉ | 477/1208 [6:22:35<9:52:55, 48.67s/it] {'loss': 0.0014, 'grad_norm': 0.16017599743386263, 'learning_rate': 6.051324503311258e-07, 'completion_length': 94.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03472900390625, 'clip_ratio': 0.0, 'epoch': 3.16} 39%|███▉ | 477/1208 [6:22:35<9:52:55, 48.67s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1018791: cache has only 0 modules Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1019664: cache has only 0 modules 40%|███▉ | 478/1208 [6:23:16<9:23:11, 46.29s/it] {'loss': 0.0016, 'grad_norm': 7.142941644605202, 'learning_rate': 6.043046357615894e-07, 'completion_length': 93.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.03912353515625, 'clip_ratio': 0.0, 'epoch': 3.17} 40%|███▉ | 478/1208 [6:23:16<9:23:11, 46.29s/it]Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1020537: cache has only 0 modules Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1021410: cache has only 0 modules 40%|███▉ | 479/1208 [6:23:54<8:51:43, 43.76s/it] {'loss': 0.0017, 'grad_norm': 5.547040590181973, 'learning_rate': 6.034768211920529e-07, 'completion_length': 87.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.042236328125, 'clip_ratio': 0.0, 'epoch': 3.17} 40%|███▉ | 479/1208 [6:23:54<8:51:43, 43.76s/it]Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1022283: cache has only 0 modules Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1023156: cache has only 0 modules 40%|███▉ | 480/1208 [6:24:34<8:38:17, 42.72s/it] {'loss': 0.0022, 'grad_norm': 10.553714089050247, 'learning_rate': 6.026490066225165e-07, 'completion_length': 99.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.05401611328125, 'clip_ratio': 0.0, 'epoch': 3.18} 40%|███▉ | 480/1208 [6:24:34<8:38:17, 42.72s/it]Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1024029: cache has only 0 modules Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1024902: cache has only 0 modules 40%|███▉ | 481/1208 [6:25:15<8:32:41, 42.31s/it] {'loss': 0.0014, 'grad_norm': 0.28047491581536405, 'learning_rate': 6.018211920529801e-07, 'completion_length': 96.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0357666015625, 'clip_ratio': 0.0, 'epoch': 3.19} 40%|███▉ | 481/1208 [6:25:15<8:32:41, 42.31s/it]Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1025775: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1026648: cache has only 0 modules 40%|███▉ | 482/1208 [6:26:01<8:45:51, 43.46s/it] {'loss': 0.0025, 'grad_norm': 7.069422632184192, 'learning_rate': 6.009933774834437e-07, 'completion_length': 100.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.06353759765625, 'clip_ratio': 0.0, 'epoch': 3.19} 40%|███▉ | 482/1208 [6:26:01<8:45:51, 43.46s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1027521: cache has only 0 modules Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1028394: cache has only 0 modules 40%|███▉ | 483/1208 [6:26:37<8:17:12, 41.15s/it] {'loss': 0.0017, 'grad_norm': 9.636298735503424, 'learning_rate': 6.001655629139073e-07, 'completion_length': 92.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.04205322265625, 'clip_ratio': 0.0, 'epoch': 3.2} 40%|███▉ | 483/1208 [6:26:37<8:17:12, 41.15s/it]Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1029267: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1030140: cache has only 0 modules 40%|████ | 484/1208 [6:27:19<8:20:02, 41.44s/it] {'loss': 0.0016, 'grad_norm': 12.940339449376438, 'learning_rate': 5.993377483443708e-07, 'completion_length': 98.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0400390625, 'clip_ratio': 0.0, 'epoch': 3.21} 40%|████ | 484/1208 [6:27:19<8:20:02, 41.44s/it]Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1031013: cache has only 0 modules Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1031886: cache has only 0 modules 40%|████ | 485/1208 [6:27:57<8:05:16, 40.27s/it] {'loss': 0.0022, 'grad_norm': 5.3066226534472785, 'learning_rate': 5.985099337748344e-07, 'completion_length': 94.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0557861328125, 'clip_ratio': 0.0, 'epoch': 3.21} 40%|████ | 485/1208 [6:27:57<8:05:16, 40.27s/it]Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1032759: cache has only 0 modules Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1033632: cache has only 0 modules 40%|████ | 486/1208 [6:28:32<7:44:40, 38.62s/it] {'loss': 0.0019, 'grad_norm': 8.254532840699744, 'learning_rate': 5.97682119205298e-07, 'completion_length': 85.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'epoch': 3.22} 40%|████ | 486/1208 [6:28:32<7:44:40, 38.62s/it]Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1034505: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1035378: cache has only 0 modules [Step 486] loss_orig = -0.352365, loss_refine = -0.586239 [Step 486] loss_orig = 2.475389, loss_refine = 0.354244 [Step 486] loss_orig = -0.351131, loss_refine = -0.585857[Step 486] loss_orig = -0.351571, loss_refine = 0.356180[Step 486] loss_orig = -0.351973, loss_refine = -0.585010 [Step 486] loss_orig = -0.351296, loss_refine = -0.587623 [Step 486] loss_orig = -0.352388, loss_refine = -0.587001 [Step 486] loss_orig = -0.351746, loss_refine = 2.240192 Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1036251: cache has only 0 modules 40%|████ | 487/1208 [6:29:46<9:52:16, 49.29s/it] {'loss': 0.0019, 'grad_norm': 77.36777441598974, 'learning_rate': 5.968543046357615e-07, 'completion_length': 116.20833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.375, 'reward_std': 0.5892556309700012, 'kl': 0.0394287109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.23} 40%|████ | 487/1208 [6:29:46<9:52:16, 49.29s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1037124: cache has only 0 modules Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1037997: cache has only 0 modules 40%|████ | 488/1208 [6:30:28<9:26:35, 47.22s/it] {'loss': 0.0035, 'grad_norm': 7.8341667114735225, 'learning_rate': 5.960264900662252e-07, 'completion_length': 93.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.08746337890625, 'clip_ratio': 0.0, 'epoch': 3.23} 40%|████ | 488/1208 [6:30:28<9:26:35, 47.22s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1038870: cache has only 0 modules Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1039743: cache has only 0 modules 40%|████ | 489/1208 [6:31:15<9:24:27, 47.10s/it] {'loss': 0.001, 'grad_norm': 0.33079993605531327, 'learning_rate': 5.951986754966887e-07, 'completion_length': 103.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02392578125, 'clip_ratio': 0.0, 'epoch': 3.24} 40%|████ | 489/1208 [6:31:15<9:24:27, 47.10s/it]Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1040616: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1041489: cache has only 0 modules 41%|████ | 490/1208 [6:32:01<9:20:29, 46.84s/it] {'loss': 0.0035, 'grad_norm': 6.307920803838524, 'learning_rate': 5.943708609271522e-07, 'completion_length': 108.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.5487885922193527, 'kl': 0.0875244140625, 'clip_ratio': 0.0, 'epoch': 3.25} 41%|████ | 490/1208 [6:32:01<9:20:29, 46.84s/it]Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1042362: cache has only 0 modules Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1043235: cache has only 0 modules 41%|████ | 491/1208 [6:32:39<8:46:27, 44.05s/it] {'loss': 0.0011, 'grad_norm': 0.24544511552437612, 'learning_rate': 5.935430463576159e-07, 'completion_length': 86.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02685546875, 'clip_ratio': 0.0, 'epoch': 3.25} 41%|████ | 491/1208 [6:32:39<8:46:27, 44.05s/it]Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1044108: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1044981: cache has only 0 modules 41%|████ | 492/1208 [6:33:19<8:33:31, 43.03s/it] {'loss': 0.0012, 'grad_norm': 3.548832061021724, 'learning_rate': 5.927152317880795e-07, 'completion_length': 89.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.02978515625, 'clip_ratio': 0.0, 'epoch': 3.26} 41%|████ | 492/1208 [6:33:19<8:33:31, 43.03s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1045854: cache has only 0 modules Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1046727: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'fold input method'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1047600: cache has only 0 modules [Step 492] loss_orig = 0.002343, loss_refine = -0.502817[Step 492] loss_orig = 0.003416, loss_refine = -0.502279 [Step 492] loss_orig = 0.000815, loss_refine = -0.502870 [Step 492] loss_orig = 0.001169, loss_refine = -0.503027[Step 492] loss_orig = 0.002628, loss_refine = -0.503134 [Step 492] loss_orig = 0.002326, loss_refine = -0.502282 [Step 492] loss_orig = 0.001730, loss_refine = 2.184932[Step 492] loss_orig = 0.002474, loss_refine = 0.841120 41%|████ | 493/1208 [6:34:22<9:42:53, 48.91s/it] {'loss': 0.0013, 'grad_norm': 15.936189452691428, 'learning_rate': 5.918874172185431e-07, 'completion_length': 96.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4205243190129598, 'kl': 0.0430908203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 3.26} 41%|████ | 493/1208 [6:34:22<9:42:53, 48.91s/it]Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1048473: cache has only 0 modules Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1049346: cache has only 0 modules 41%|████ | 494/1208 [6:34:56<8:48:18, 44.40s/it] {'loss': 0.0013, 'grad_norm': 85.64304969679019, 'learning_rate': 5.910596026490065e-07, 'completion_length': 87.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.03167724609375, 'clip_ratio': 0.0, 'epoch': 3.27} 41%|████ | 494/1208 [6:34:56<8:48:18, 44.40s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1050219: cache has only 0 modules Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1051092: cache has only 0 modules 41%|████ | 495/1208 [6:35:32<8:16:53, 41.81s/it] {'loss': 0.0019, 'grad_norm': 4.696583684575833, 'learning_rate': 5.902317880794702e-07, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.0482177734375, 'clip_ratio': 0.0, 'epoch': 3.28} 41%|████ | 495/1208 [6:35:32<8:16:53, 41.81s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1051965: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1263, 613]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1052838: cache has only 0 modules [Step 495] loss_orig = 0.001691, loss_refine = 0.542121[Step 495] loss_orig = 0.000942, loss_refine = 0.540937 [Step 495] loss_orig = 0.000594, loss_refine = 0.541096[Step 495] loss_orig = 0.001246, loss_refine = 0.541606 [Step 495] loss_orig = 0.000845, loss_refine = 0.541109 [Step 495] loss_orig = 0.001837, loss_refine = -1.618852[Step 495] loss_orig = 0.001382, loss_refine = 0.541868 [Step 495] loss_orig = 0.000947, loss_refine = -1.618292 Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1053711: cache has only 0 modules 41%|████ | 496/1208 [6:36:31<9:18:32, 47.07s/it] {'loss': 0.0019, 'grad_norm': 11.137275317880382, 'learning_rate': 5.894039735099338e-07, 'completion_length': 105.04166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.30860670407613117, 'kl': 0.04339599609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.28} 41%|████ | 496/1208 [6:36:31<9:18:32, 47.07s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1054584: cache has only 0 modules Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1055457: cache has only 0 modules 41%|████ | 497/1208 [6:37:07<8:39:47, 43.86s/it] {'loss': 0.0016, 'grad_norm': 5.116092437865746, 'learning_rate': 5.885761589403973e-07, 'completion_length': 92.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.041015625, 'clip_ratio': 0.0, 'epoch': 3.29} 41%|████ | 497/1208 [6:37:07<8:39:47, 43.86s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1056330: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. Your previous answer was [{'action': 'click', 'coordinate': [141, 1539]}, {'action': 'click', 'coordinate': [358, 760]}, {'action': 'click', 'coordinate': [1600, 1174]}, {'action': 'click', 'coordinate': [422, 1255]}, {'action': 'click', 'coordinate': [831, 1340]}, {'action': 'click', 'coordinate': [446, 1425]}, {'action': 'click', 'coordinate': [600, 1527]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxdiff coord reward errorcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1057203: cache has only 0 modules [Step 497] loss_orig = -0.352388, loss_refine = -0.147656 [Step 497] loss_orig = -0.350719, loss_refine = -1.345826 [Step 497] loss_orig = -0.351156, loss_refine = -0.143418 [Step 497] loss_orig = -0.351246, loss_refine = -0.147969 [Step 497] loss_orig = -0.349708, loss_refine = -0.147911 [Step 497] loss_orig = -0.350906, loss_refine = -0.148830 [Step 497] loss_orig = 2.478068, loss_refine = -0.147465[Step 497] loss_orig = -0.351125, loss_refine = 2.247439 Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1058076: cache has only 0 modules 41%|████ | 498/1208 [6:38:28<10:47:50, 54.75s/it] {'loss': 0.0017, 'grad_norm': 4.016097687322832, 'learning_rate': 5.877483443708608e-07, 'completion_length': 132.91666666666666, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.25, 'reward_std': 0.3960254490375519, 'kl': 0.04656982421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 3.3} 41%|████ | 498/1208 [6:38:28<10:47:50, 54.75s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1058949: cache has only 0 modules Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1059822: cache has only 0 modules 41%|████▏ | 499/1208 [6:39:14<10:18:36, 52.35s/it] {'loss': 0.0014, 'grad_norm': 8.470689981431383, 'learning_rate': 5.869205298013245e-07, 'completion_length': 95.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.03497314453125, 'clip_ratio': 0.0, 'epoch': 3.3} 41%|████▏ | 499/1208 [6:39:14<10:18:36, 52.35s/it]Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1060695: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1061568: cache has only 0 modules [Step 499] loss_orig = 0.000662, loss_refine = 0.000564[Step 499] loss_orig = 0.001050, loss_refine = 0.000768 [Step 499] loss_orig = 0.000412, loss_refine = 0.002101[Step 499] loss_orig = 0.000903, loss_refine = 0.000246 [Step 499] loss_orig = 0.000823, loss_refine = 0.000537 [Step 499] loss_orig = 0.000697, loss_refine = 0.000895 [Step 499] loss_orig = 0.001023, loss_refine = 0.001375 [Step 499] loss_orig = 0.001125, loss_refine = 0.000990 Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1062441: cache has only 0 modules 41%|████▏ | 500/1208 [6:40:19<11:02:29, 56.14s/it] {'loss': 0.001, 'grad_norm': 0.23285975674807352, 'learning_rate': 5.860927152317881e-07, 'completion_length': 94.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.023681640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 3.31} 41%|████▏ | 500/1208 [6:40:19<11:02:29, 56.14s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1063314: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1064187: cache has only 0 modules 41%|████▏ | 501/1208 [6:41:01<10:11:15, 51.87s/it] {'loss': 0.0009, 'grad_norm': 5.741045650291375, 'learning_rate': 5.852649006622516e-07, 'completion_length': 95.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0234375, 'clip_ratio': 0.0, 'epoch': 3.32} 41%|████▏ | 501/1208 [6:41:01<10:11:15, 51.87s/it]Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1065060: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1065933: cache has only 0 modules [Step 501] loss_orig = 0.001237, loss_refine = 0.726134 [Step 501] loss_orig = 0.001662, loss_refine = 0.725196 [Step 501] loss_orig = 0.000989, loss_refine = 0.725246 [Step 501] loss_orig = 0.001966, loss_refine = -1.206396 [Step 501] loss_orig = 0.003472, loss_refine = -1.206010 [Step 501] loss_orig = 0.001710, loss_refine = -1.206023 [Step 501] loss_orig = 0.001138, loss_refine = 0.725841 [Step 501] loss_orig = 0.000814, loss_refine = 0.725805 Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1066806: cache has only 0 modules 42%|████▏ | 502/1208 [6:41:59<10:32:49, 53.78s/it] {'loss': 0.0014, 'grad_norm': 5.302539165820326, 'learning_rate': 5.844370860927152e-07, 'completion_length': 96.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.3268197377522786, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 3.32} 42%|████▏ | 502/1208 [6:41:59<10:32:49, 53.78s/it]Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1067679: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1068552: cache has only 0 modules 42%|████▏ | 503/1208 [6:42:54<10:34:11, 53.97s/it] {'loss': 0.001, 'grad_norm': 1.6244791987693836, 'learning_rate': 5.836092715231788e-07, 'completion_length': 106.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.5303300619125366, 'kl': 0.02532958984375, 'clip_ratio': 0.0, 'epoch': 3.33} 42%|████▏ | 503/1208 [6:42:54<10:34:11, 53.97s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1069425: cache has only 0 modules Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1070298: cache has only 0 modules 42%|████▏ | 504/1208 [6:43:32<9:36:44, 49.15s/it] {'loss': 0.0033, 'grad_norm': 7.222514505223165, 'learning_rate': 5.827814569536423e-07, 'completion_length': 86.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.081787109375, 'clip_ratio': 0.0, 'epoch': 3.34} 42%|████▏ | 504/1208 [6:43:32<9:36:44, 49.15s/it]Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1071171: cache has only 0 modules Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1072044: cache has only 0 modules 42%|████▏ | 505/1208 [6:44:24<9:45:11, 49.94s/it] {'loss': 0.0019, 'grad_norm': 5.108477198628453, 'learning_rate': 5.819536423841059e-07, 'completion_length': 115.4375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.5625, 'reward_std': 0.7975912988185883, 'kl': 0.047119140625, 'clip_ratio': 0.0, 'epoch': 3.34} 42%|████▏ | 505/1208 [6:44:24<9:45:11, 49.94s/it]Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1072917: cache has only 0 modules Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1073790: cache has only 0 modules 42%|████▏ | 506/1208 [6:45:00<8:56:14, 45.83s/it] {'loss': 0.0016, 'grad_norm': 4.430893500487733, 'learning_rate': 5.811258278145696e-07, 'completion_length': 84.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0404052734375, 'clip_ratio': 0.0, 'epoch': 3.35} 42%|████▏ | 506/1208 [6:45:00<8:56:14, 45.83s/it]Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1074663: cache has only 0 modules Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1075536: cache has only 0 modules 42%|████▏ | 507/1208 [6:45:40<8:36:22, 44.20s/it] {'loss': 0.0027, 'grad_norm': 4.7634909611361085, 'learning_rate': 5.802980132450332e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.068115234375, 'clip_ratio': 0.0, 'epoch': 3.36} 42%|████▏ | 507/1208 [6:45:40<8:36:22, 44.20s/it]Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1076409: cache has only 0 modules Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1077282: cache has only 0 modules 42%|████▏ | 508/1208 [6:46:16<8:06:32, 41.70s/it] {'loss': 0.0017, 'grad_norm': 0.5515063578473364, 'learning_rate': 5.794701986754966e-07, 'completion_length': 89.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04248046875, 'clip_ratio': 0.0, 'epoch': 3.36} 42%|████▏ | 508/1208 [6:46:16<8:06:32, 41.70s/it]Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1078155: cache has only 0 modules Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1079028: cache has only 0 modules 42%|████▏ | 509/1208 [6:46:52<7:44:11, 39.84s/it] {'loss': 0.0009, 'grad_norm': 6.778899932958552, 'learning_rate': 5.786423841059602e-07, 'completion_length': 86.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.023223876953125, 'clip_ratio': 0.0, 'epoch': 3.37} 42%|████▏ | 509/1208 [6:46:52<7:44:11, 39.84s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1079901: cache has only 0 modules Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1080774: cache has only 0 modules 42%|████▏ | 510/1208 [6:47:38<8:05:41, 41.75s/it] {'loss': 0.0025, 'grad_norm': 9.133126420395067, 'learning_rate': 5.778145695364239e-07, 'completion_length': 94.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0623779296875, 'clip_ratio': 0.0, 'epoch': 3.38} 42%|████▏ | 510/1208 [6:47:38<8:05:41, 41.75s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1081647: cache has only 0 modules Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1082520: cache has only 0 modules 42%|████▏ | 511/1208 [6:48:18<7:59:18, 41.26s/it] {'loss': 0.0016, 'grad_norm': 7.495902865074118, 'learning_rate': 5.769867549668874e-07, 'completion_length': 95.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.040771484375, 'clip_ratio': 0.0, 'epoch': 3.38} 42%|████▏ | 511/1208 [6:48:18<7:59:18, 41.26s/it]Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1083393: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element deserts'. Your previous answer was [{'action': 'click', 'coordinate': [1053, 519]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1084266: cache has only 0 modules [Step 511] loss_orig = 0.000899, loss_refine = 1.438080 [Step 511] loss_orig = 0.001814, loss_refine = -1.155759[Step 511] loss_orig = 0.001526, loss_refine = 0.531532 [Step 511] loss_orig = 0.002108, loss_refine = 0.528544[Step 511] loss_orig = 0.001450, loss_refine = -1.155789 [Step 511] loss_orig = 0.001619, loss_refine = 0.527588 [Step 511] loss_orig = 0.002135, loss_refine = 0.528684 [Step 511] loss_orig = 0.001759, loss_refine = -1.155893 Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1085139: cache has only 0 modules 42%|████▏ | 512/1208 [6:49:17<9:02:01, 46.73s/it] {'loss': 0.0061, 'grad_norm': 6.024122184872808, 'learning_rate': 5.761589403973509e-07, 'completion_length': 99.16666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3959116538365682, 'kl': 0.0367431640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 3.39} 42%|████▏ | 512/1208 [6:49:17<9:02:01, 46.73s/it]Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1086012: cache has only 0 modules Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1086885: cache has only 0 modules 42%|████▏ | 513/1208 [6:49:58<8:41:10, 44.99s/it] {'loss': 0.0028, 'grad_norm': 7.570433238850915, 'learning_rate': 5.753311258278145e-07, 'completion_length': 99.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.0699462890625, 'clip_ratio': 0.0, 'epoch': 3.4} 42%|████▏ | 513/1208 [6:49:58<8:41:10, 44.99s/it]Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1087758: cache has only 0 modules Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1088631: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'manage the outlayer'. Your previous answer was [{'action': 'click', 'coordinate': [853, 340]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1089504: cache has only 0 modules [Step 513] loss_orig = 0.002051, loss_refine = 1.349731 [Step 513] loss_orig = 0.001892, loss_refine = 0.151256[Step 513] loss_orig = 0.005849, loss_refine = 0.151041 [Step 513] loss_orig = 0.002255, loss_refine = -1.047054 [Step 513] loss_orig = 0.001491, loss_refine = 0.151019[Step 513] loss_orig = 0.004499, loss_refine = -1.047086 [Step 513] loss_orig = 0.004183, loss_refine = 1.350192 [Step 513] loss_orig = 0.002268, loss_refine = -1.047084 43%|████▎ | 514/1208 [6:51:05<9:55:55, 51.52s/it] {'loss': 0.002, 'grad_norm': 15.334916520035057, 'learning_rate': 5.745033112582781e-07, 'completion_length': 114.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.4563484787940979, 'kl': 0.0692138671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 3.4} 43%|████▎ | 514/1208 [6:51:05<9:55:55, 51.52s/it]Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1090377: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1091250: cache has only 0 modules 43%|████▎ | 515/1208 [6:51:37<8:45:54, 45.53s/it] {'loss': 0.0023, 'grad_norm': 19.04455824124237, 'learning_rate': 5.736754966887417e-07, 'completion_length': 92.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.0579833984375, 'clip_ratio': 0.0, 'epoch': 3.41} 43%|████▎ | 515/1208 [6:51:37<8:45:54, 45.53s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1092123: cache has only 0 modules Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1092996: cache has only 0 modules 43%|████▎ | 516/1208 [6:52:14<8:16:07, 43.02s/it] {'loss': 0.0035, 'grad_norm': 7.969494711996626, 'learning_rate': 5.728476821192053e-07, 'completion_length': 92.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.086181640625, 'clip_ratio': 0.0, 'epoch': 3.42} 43%|████▎ | 516/1208 [6:52:14<8:16:07, 43.02s/it]Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1093869: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1094742: cache has only 0 modules 43%|████▎ | 517/1208 [6:52:58<8:20:26, 43.45s/it] {'loss': 0.0021, 'grad_norm': 6.832107645930456, 'learning_rate': 5.720198675496689e-07, 'completion_length': 101.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.0533447265625, 'clip_ratio': 0.0, 'epoch': 3.42} 43%|████▎ | 517/1208 [6:52:58<8:20:26, 43.45s/it]Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1095615: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: topic-link-a151002'. Your previous answer was [{'action': 'click', 'coordinate': [1606, 495]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1096488: cache has only 0 modules [Step 517] loss_orig = 0.002219, loss_refine = -1.663622[Step 517] loss_orig = 0.000753, loss_refine = -0.774544[Step 517] loss_orig = 0.001546, loss_refine = 0.112199 [Step 517] loss_orig = 0.001884, loss_refine = 0.112171 [Step 517] loss_orig = 0.000888, loss_refine = 0.112218 [Step 517] loss_orig = 0.001247, loss_refine = 0.112725 [Step 517] loss_orig = 0.002192, loss_refine = 0.112715 [Step 517] loss_orig = 0.001547, loss_refine = 1.889288 Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1097361: cache has only 0 modules 43%|████▎ | 518/1208 [6:54:06<9:44:34, 50.83s/it] {'loss': 0.0014, 'grad_norm': 6.908139164041521, 'learning_rate': 5.711920529801324e-07, 'completion_length': 115.875, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.0416666666666665, 'reward_std': 0.6839372714360555, 'kl': 0.03448486328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.43} 43%|████▎ | 518/1208 [6:54:06<9:44:34, 50.83s/it]Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1098234: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1099107: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Height'. Your previous answer was [{'action': 'click', 'coordinate': [560, 95]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1099980: cache has only 0 modules [Step 518] loss_orig = 0.002392, loss_refine = -0.407538 [Step 518] loss_orig = 0.000964, loss_refine = -1.499107[Step 518] loss_orig = 0.003256, loss_refine = 0.686993 [Step 518] loss_orig = 0.000720, loss_refine = -0.407757 [Step 518] loss_orig = 0.002379, loss_refine = 0.685310 [Step 518] loss_orig = 0.001945, loss_refine = -0.406830 [Step 518] loss_orig = 0.001178, loss_refine = -0.407502 [Step 518] loss_orig = 0.000781, loss_refine = 1.775360 43%|████▎ | 519/1208 [6:55:22<11:09:55, 58.34s/it] {'loss': 0.0024, 'grad_norm': 9.821928566224992, 'learning_rate': 5.70364238410596e-07, 'completion_length': 104.04166666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.0, 'reward_std': 0.4778915246327718, 'kl': 0.0521240234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 3.44} 43%|████▎ | 519/1208 [6:55:22<11:09:55, 58.34s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1100853: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1101726: cache has only 0 modules 43%|████▎ | 520/1208 [6:56:09<10:29:21, 54.89s/it] {'loss': 0.0025, 'grad_norm': 7.125741060769959, 'learning_rate': 5.695364238410596e-07, 'completion_length': 115.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.06292724609375, 'clip_ratio': 0.0, 'epoch': 3.44} 43%|████▎ | 520/1208 [6:56:09<10:29:21, 54.89s/it]Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1102599: cache has only 0 modules Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1103472: cache has only 0 modules 43%|████▎ | 521/1208 [6:57:12<10:56:49, 57.36s/it] {'loss': 0.0015, 'grad_norm': 9.233230987551442, 'learning_rate': 5.687086092715232e-07, 'completion_length': 136.25, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.875, 'reward': 2.125, 'reward_std': 0.9475915431976318, 'kl': 0.03741455078125, 'clip_ratio': 0.0, 'epoch': 3.45} 43%|████▎ | 521/1208 [6:57:12<10:56:49, 57.36s/it]Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1104345: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1105218: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Line History View, group'. Your previous answer was [{'action': 'click', 'coordinate': [987, 264]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1106091: cache has only 0 modules [Step 521] loss_orig = -0.538248, loss_refine = 0.665897[Step 521] loss_orig = -0.537766, loss_refine = 0.662776[Step 521] loss_orig = -0.538810, loss_refine = -0.658137[Step 521] loss_orig = 1.622014, loss_refine = -1.980874 [Step 521] loss_orig = -0.535611, loss_refine = 0.665176 [Step 521] loss_orig = 1.621186, loss_refine = -0.660302[Step 521] loss_orig = -0.537666, loss_refine = 0.663217 [Step 521] loss_orig = -0.538820, loss_refine = 0.662712 43%|████▎ | 522/1208 [6:58:20<11:32:10, 60.54s/it] {'loss': 0.0019, 'grad_norm': 7.0278079428033555, 'learning_rate': 5.678807947019867e-07, 'completion_length': 119.45833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.3333333333333335, 'reward_std': 0.5605830152829488, 'kl': 0.03985595703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.46} 43%|████▎ | 522/1208 [6:58:20<11:32:10, 60.54s/it]Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1106964: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1107837: cache has only 0 modules 43%|████▎ | 523/1208 [6:58:52<9:54:14, 52.05s/it] {'loss': 0.0014, 'grad_norm': 8.503702570424098, 'learning_rate': 5.670529801324503e-07, 'completion_length': 76.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0340576171875, 'clip_ratio': 0.0, 'epoch': 3.46} 43%|████▎ | 523/1208 [6:58:52<9:54:14, 52.05s/it]Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1108710: cache has only 0 modules Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1109583: cache has only 0 modules 43%|████▎ | 524/1208 [6:59:39<9:36:43, 50.59s/it] {'loss': 0.0012, 'grad_norm': 10.53491008788382, 'learning_rate': 5.662251655629138e-07, 'completion_length': 88.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.030029296875, 'clip_ratio': 0.0, 'epoch': 3.47} 43%|████▎ | 524/1208 [6:59:39<9:36:43, 50.59s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1110456: cache has only 0 modules Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1111329: cache has only 0 modules 43%|████▎ | 525/1208 [7:00:16<8:48:22, 46.42s/it] {'loss': 0.0013, 'grad_norm': 3.7522747253149906, 'learning_rate': 5.653973509933775e-07, 'completion_length': 99.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0318603515625, 'clip_ratio': 0.0, 'epoch': 3.48} 43%|████▎ | 525/1208 [7:00:16<8:48:22, 46.42s/it]Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1112202: cache has only 0 modules Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1113075: cache has only 0 modules 44%|████▎ | 526/1208 [7:01:09<9:09:36, 48.35s/it] {'loss': 0.0011, 'grad_norm': 10.887175106968675, 'learning_rate': 5.645695364238411e-07, 'completion_length': 101.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.7891046404838562, 'kl': 0.0286865234375, 'clip_ratio': 0.0, 'epoch': 3.48} 44%|████▎ | 526/1208 [7:01:09<9:09:36, 48.35s/it]Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1113948: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1114821: cache has only 0 modules 44%|████▎ | 527/1208 [7:01:43<8:19:01, 43.97s/it] {'loss': 0.0015, 'grad_norm': 0.3296316126512934, 'learning_rate': 5.637417218543046e-07, 'completion_length': 89.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03802490234375, 'clip_ratio': 0.0, 'epoch': 3.49} 44%|████▎ | 527/1208 [7:01:43<8:19:01, 43.97s/it]Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1115694: cache has only 0 modules Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1116567: cache has only 0 modules 44%|████▎ | 528/1208 [7:02:29<8:25:06, 44.57s/it] {'loss': 0.0011, 'grad_norm': 13.666303088848917, 'learning_rate': 5.629139072847681e-07, 'completion_length': 102.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.02630615234375, 'clip_ratio': 0.0, 'epoch': 3.5} 44%|████▎ | 528/1208 [7:02:29<8:25:06, 44.57s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1117440: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1118313: cache has only 0 modules [Step 528] loss_orig = -0.538064, loss_refine = -0.537916 [Step 528] loss_orig = -0.538541, loss_refine = -0.538191 [Step 528] loss_orig = -0.538786, loss_refine = 1.620918[Step 528] loss_orig = -0.539320, loss_refine = -0.537477[Step 528] loss_orig = -0.538532, loss_refine = -0.538158 [Step 528] loss_orig = -0.538251, loss_refine = -0.538856[Step 528] loss_orig = 1.621734, loss_refine = -0.538582 [Step 528] loss_orig = 1.620931, loss_refine = 1.622819 Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1119186: cache has only 0 modules 44%|████▍ | 529/1208 [7:03:34<9:35:21, 50.84s/it] {'loss': 0.0015, 'grad_norm': 4.608095236243508, 'learning_rate': 5.620860927152318e-07, 'completion_length': 114.58333333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.4166666666666665, 'reward_std': 0.4629100561141968, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 3.5} 44%|████▍ | 529/1208 [7:03:34<9:35:21, 50.84s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1120059: cache has only 0 modules Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1120932: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. Your previous answer was [{'action': 'click', 'coordinate': [1795, 16]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1121805: cache has only 0 modules [Step 529] loss_orig = 0.001166, loss_refine = -0.933791 [Step 529] loss_orig = 0.003153, loss_refine = 0.937529[Step 529] loss_orig = 0.001747, loss_refine = 0.949450 [Step 529] loss_orig = 0.002741, loss_refine = 0.936693 [Step 529] loss_orig = 0.001494, loss_refine = -0.933531 [Step 529] loss_orig = 0.001900, loss_refine = -0.934094[Step 529] loss_orig = 0.002474, loss_refine = 0.938886 [Step 529] loss_orig = 0.002396, loss_refine = -0.934256 44%|████▍ | 530/1208 [7:04:37<10:14:14, 54.36s/it] {'loss': 0.0026, 'grad_norm': 9.438914706651754, 'learning_rate': 5.612582781456954e-07, 'completion_length': 112.45833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.1666666666666665, 'reward_std': 0.43015046914418537, 'kl': 0.0496826171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 3.51} 44%|████▍ | 530/1208 [7:04:37<10:14:14, 54.36s/it]Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1122678: cache has only 0 modules Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1123551: cache has only 0 modules 44%|████▍ | 531/1208 [7:05:16<9:22:08, 49.82s/it] {'loss': 0.0012, 'grad_norm': 29.736333402032205, 'learning_rate': 5.604304635761588e-07, 'completion_length': 103.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.02996826171875, 'clip_ratio': 0.0, 'epoch': 3.52} 44%|████▍ | 531/1208 [7:05:16<9:22:08, 49.82s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1124424: cache has only 0 modules Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1125297: cache has only 0 modules 44%|████▍ | 532/1208 [7:06:03<9:13:11, 49.10s/it] {'loss': 0.0033, 'grad_norm': 6.391648197295783, 'learning_rate': 5.596026490066225e-07, 'completion_length': 99.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0819091796875, 'clip_ratio': 0.0, 'epoch': 3.52} 44%|████▍ | 532/1208 [7:06:03<9:13:11, 49.10s/it]Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1126170: cache has only 0 modules Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1127043: cache has only 0 modules 44%|████▍ | 533/1208 [7:06:38<8:22:15, 44.65s/it] {'loss': 0.0016, 'grad_norm': 8.185083012744869, 'learning_rate': 5.587748344370861e-07, 'completion_length': 83.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0399169921875, 'clip_ratio': 0.0, 'epoch': 3.53} 44%|████▍ | 533/1208 [7:06:38<8:22:15, 44.65s/it]Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1127916: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1128789: cache has only 0 modules [Step 533] loss_orig = 0.001507, loss_refine = 0.002356[Step 533] loss_orig = 0.002730, loss_refine = 0.001960 [Step 533] loss_orig = 0.001250, loss_refine = 0.001787 [Step 533] loss_orig = 0.001681, loss_refine = 0.001199 [Step 533] loss_orig = 0.001662, loss_refine = 0.002763 [Step 533] loss_orig = 0.002775, loss_refine = 0.002603 [Step 533] loss_orig = 0.000945, loss_refine = 0.002416 [Step 533] loss_orig = 0.002201, loss_refine = 0.001506 Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1129662: cache has only 0 modules 44%|████▍ | 534/1208 [7:07:41<9:25:20, 50.33s/it] {'loss': 0.0019, 'grad_norm': 12.158826706117624, 'learning_rate': 5.579470198675496e-07, 'completion_length': 110.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.043701171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.54} 44%|████▍ | 534/1208 [7:07:41<9:25:20, 50.33s/it]Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1130535: cache has only 0 modules Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1131408: cache has only 0 modules 44%|████▍ | 535/1208 [7:08:12<8:19:08, 44.50s/it] {'loss': 0.0018, 'grad_norm': 12.288217408269055, 'learning_rate': 5.571192052980132e-07, 'completion_length': 82.5, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.6034669280052185, 'kl': 0.0460205078125, 'clip_ratio': 0.0, 'epoch': 3.54} 44%|████▍ | 535/1208 [7:08:12<8:19:08, 44.50s/it]Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1132281: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1133154: cache has only 0 modules [Step 535] loss_orig = 0.002299, loss_refine = 0.001210[Step 535] loss_orig = 0.001111, loss_refine = 0.001164 [Step 535] loss_orig = 0.001342, loss_refine = 0.001966[Step 535] loss_orig = 0.000716, loss_refine = 0.000978 [Step 535] loss_orig = 0.001068, loss_refine = 0.001056[Step 535] loss_orig = 0.001068, loss_refine = 0.001438 [Step 535] loss_orig = 0.001419, loss_refine = 0.000720 [Step 535] loss_orig = 0.001215, loss_refine = 0.001139 Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1134027: cache has only 0 modules 44%|████▍ | 536/1208 [7:09:21<9:41:15, 51.90s/it] {'loss': 0.0019, 'grad_norm': 4.781453002455674, 'learning_rate': 5.562913907284768e-07, 'completion_length': 103.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.0477294921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.55} 44%|████▍ | 536/1208 [7:09:21<9:41:15, 51.90s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1134900: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1135773: cache has only 0 modules 44%|████▍ | 537/1208 [7:10:13<9:39:47, 51.84s/it] {'loss': 0.0017, 'grad_norm': 7.897875258933225, 'learning_rate': 5.554635761589404e-07, 'completion_length': 113.3125, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.875, 'reward': 2.3125, 'reward_std': 0.5303300619125366, 'kl': 0.043701171875, 'clip_ratio': 0.0, 'epoch': 3.56} 44%|████▍ | 537/1208 [7:10:13<9:39:47, 51.84s/it]Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1136646: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1137519: cache has only 0 modules 45%|████▍ | 538/1208 [7:10:47<8:37:40, 46.36s/it] {'loss': 0.0014, 'grad_norm': 0.5150804180653296, 'learning_rate': 5.546357615894039e-07, 'completion_length': 78.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'epoch': 3.56} 45%|████▍ | 538/1208 [7:10:47<8:37:40, 46.36s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1138392: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1139265: cache has only 0 modules 45%|████▍ | 539/1208 [7:11:19<7:51:26, 42.28s/it] {'loss': 0.0017, 'grad_norm': 85.01406081311721, 'learning_rate': 5.538079470198675e-07, 'completion_length': 88.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.041748046875, 'clip_ratio': 0.0, 'epoch': 3.57} 45%|████▍ | 539/1208 [7:11:19<7:51:26, 42.28s/it]Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1140138: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1141011: cache has only 0 modules 45%|████▍ | 540/1208 [7:11:59<7:42:55, 41.58s/it] {'loss': 0.0025, 'grad_norm': 10.420626243010451, 'learning_rate': 5.529801324503312e-07, 'completion_length': 98.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.06146240234375, 'clip_ratio': 0.0, 'epoch': 3.58} 45%|████▍ | 540/1208 [7:11:59<7:42:55, 41.58s/it]Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1141884: cache has only 0 modules Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1142757: cache has only 0 modules 45%|████▍ | 541/1208 [7:12:40<7:38:29, 41.24s/it] {'loss': 0.0011, 'grad_norm': 5.632825436245977, 'learning_rate': 5.521523178807946e-07, 'completion_length': 96.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02838134765625, 'clip_ratio': 0.0, 'epoch': 3.58} 45%|████▍ | 541/1208 [7:12:40<7:38:29, 41.24s/it]Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1143630: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'choose watercolor brush style'. Your previous answer was [{'action': 'click', 'coordinate': [303, 2313] }] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1144503: cache has only 0 modules [Step 541] loss_orig = -0.351212, loss_refine = 0.354199 [Step 541] loss_orig = -0.349992, loss_refine = 0.368900[Step 541] loss_orig = -0.351758, loss_refine = 0.354382 [Step 541] loss_orig = -0.351800, loss_refine = 0.356318 [Step 541] loss_orig = -0.352310, loss_refine = 0.354240 [Step 541] loss_orig = 2.478193, loss_refine = -2.473171 [Step 541] loss_orig = -0.352657, loss_refine = 0.354564 [Step 541] loss_orig = -0.352448, loss_refine = 0.354681 Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1145376: cache has only 0 modules 45%|████▍ | 542/1208 [7:13:34<8:22:17, 45.25s/it] {'loss': 0.0036, 'grad_norm': 7.44842056654024, 'learning_rate': 5.513245033112582e-07, 'completion_length': 102.70833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.5260697702566782, 'kl': 0.0762939453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.59} 45%|████▍ | 542/1208 [7:13:34<8:22:17, 45.25s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1146249: cache has only 0 modules Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1147122: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1147995: cache has only 0 modules [Step 542] loss_orig = 0.000768, loss_refine = 1.209700 [Step 542] loss_orig = 0.000741, loss_refine = -0.723034[Step 542] loss_orig = 0.000602, loss_refine = -0.723357[Step 542] loss_orig = 0.002010, loss_refine = -0.723373 [Step 542] loss_orig = 0.001086, loss_refine = 1.208251 [Step 542] loss_orig = 0.000865, loss_refine = 1.208705 [Step 542] loss_orig = 0.001655, loss_refine = -0.723352 [Step 542] loss_orig = 0.000926, loss_refine = -0.723598 45%|████▍ | 543/1208 [7:14:26<8:43:47, 47.26s/it] {'loss': 0.0017, 'grad_norm': 10.079489040773463, 'learning_rate': 5.504966887417219e-07, 'completion_length': 93.33333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4205243190129598, 'kl': 0.0394287109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.6} 45%|████▍ | 543/1208 [7:14:26<8:43:47, 47.26s/it]Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1148868: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1149741: cache has only 0 modules 45%|████▌ | 544/1208 [7:15:10<8:32:38, 46.32s/it] {'loss': 0.0031, 'grad_norm': 0.2701918915390284, 'learning_rate': 5.496688741721855e-07, 'completion_length': 108.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.07720947265625, 'clip_ratio': 0.0, 'epoch': 3.6} 45%|████▌ | 544/1208 [7:15:10<8:32:38, 46.32s/it]Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1150614: cache has only 0 modules Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1151487: cache has only 0 modules 45%|████▌ | 545/1208 [7:15:52<8:17:20, 45.01s/it] {'loss': 0.0016, 'grad_norm': 4.513492638850907, 'learning_rate': 5.48841059602649e-07, 'completion_length': 100.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.038818359375, 'clip_ratio': 0.0, 'epoch': 3.61} 45%|████▌ | 545/1208 [7:15:52<8:17:20, 45.01s/it]Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1152360: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1153233: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_ArrowCircle_M'. Your previous answer was [{'action': 'click', 'coordinate': [341, 898]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1154106: cache has only 0 modules [Step 545] loss_orig = 0.001717, loss_refine = 0.841528 [Step 545] loss_orig = 0.001315, loss_refine = -0.500770 [Step 545] loss_orig = 0.001291, loss_refine = -0.499327 [Step 545] loss_orig = 0.003893, loss_refine = -0.502263 [Step 545] loss_orig = 0.003079, loss_refine = -0.501986 [Step 545] loss_orig = 0.001334, loss_refine = -0.499602 [Step 545] loss_orig = 0.001479, loss_refine = -0.501536 [Step 545] loss_orig = 0.001672, loss_refine = 2.185401 45%|████▌ | 546/1208 [7:17:08<9:58:22, 54.23s/it] {'loss': 0.002, 'grad_norm': 5.8992135248748445, 'learning_rate': 5.480132450331125e-07, 'completion_length': 117.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.24800793329874674, 'kl': 0.0419921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 3.62} 45%|████▌ | 546/1208 [7:17:08<9:58:22, 54.23s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1154979: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1155852: cache has only 0 modules 45%|████▌ | 547/1208 [7:17:48<9:09:12, 49.85s/it] {'loss': 0.0012, 'grad_norm': 10.643545418033813, 'learning_rate': 5.471854304635762e-07, 'completion_length': 94.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0306396484375, 'clip_ratio': 0.0, 'epoch': 3.62} 45%|████▌ | 547/1208 [7:17:48<9:09:12, 49.85s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1156725: cache has only 0 modules Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1157598: cache has only 0 modules 45%|████▌ | 548/1208 [7:18:28<8:37:20, 47.03s/it] {'loss': 0.0015, 'grad_norm': 11.857186529152266, 'learning_rate': 5.463576158940397e-07, 'completion_length': 100.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.03778076171875, 'clip_ratio': 0.0, 'epoch': 3.63} 45%|████▌ | 548/1208 [7:18:28<8:37:20, 47.03s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1158471: cache has only 0 modules Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1159344: cache has only 0 modules 45%|████▌ | 549/1208 [7:19:05<8:03:28, 44.02s/it] {'loss': 0.0027, 'grad_norm': 6.708945422012715, 'learning_rate': 5.455298013245033e-07, 'completion_length': 88.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.3535533845424652, 'kl': 0.0670166015625, 'clip_ratio': 0.0, 'epoch': 3.64} 45%|████▌ | 549/1208 [7:19:05<8:03:28, 44.02s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1160217: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1161090: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [599, 1396]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1161963: cache has only 0 modules [Step 549] loss_orig = 0.001281, loss_refine = 0.542375 [Step 549] loss_orig = 0.001072, loss_refine = 0.541424 [Step 549] loss_orig = 0.001177, loss_refine = 0.541152 [Step 549] loss_orig = 0.001885, loss_refine = 0.541495[Step 549] loss_orig = 0.004929, loss_refine = -1.617975 [Step 549] loss_orig = 0.002302, loss_refine = -1.618905 [Step 549] loss_orig = 0.001040, loss_refine = 0.542596 [Step 549] loss_orig = 0.002990, loss_refine = 0.541859 46%|████▌ | 550/1208 [7:20:15<9:25:55, 51.60s/it] {'loss': 0.0017, 'grad_norm': 11.262972830721266, 'learning_rate': 5.447019867549668e-07, 'completion_length': 105.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.1666666666666665, 'reward_std': 0.30860670407613117, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.64} 46%|████▌ | 550/1208 [7:20:15<9:25:55, 51.60s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1162836: cache has only 0 modules Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1163709: cache has only 0 modules 46%|████▌ | 551/1208 [7:20:47<8:22:02, 45.85s/it] {'loss': 0.0016, 'grad_norm': 150.72763546352735, 'learning_rate': 5.438741721854304e-07, 'completion_length': 79.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0394287109375, 'clip_ratio': 0.0, 'epoch': 3.65} 46%|████▌ | 551/1208 [7:20:47<8:22:02, 45.85s/it]Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1164582: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1165455: cache has only 0 modules 46%|████▌ | 552/1208 [7:21:18<7:34:15, 41.55s/it] {'loss': 0.0018, 'grad_norm': 6.83106827957174, 'learning_rate': 5.43046357615894e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.04541015625, 'clip_ratio': 0.0, 'epoch': 3.66} 46%|████▌ | 552/1208 [7:21:18<7:34:15, 41.55s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1166328: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1167201: cache has only 0 modules [Step 552] loss_orig = 0.000906, loss_refine = 0.541942[Step 552] loss_orig = 0.001290, loss_refine = 0.541575[Step 552] loss_orig = 0.001928, loss_refine = 0.542986[Step 552] loss_orig = 0.001500, loss_refine = 0.542453[Step 552] loss_orig = 0.001917, loss_refine = -1.618405[Step 552] loss_orig = 0.001605, loss_refine = 0.548708 [Step 552] loss_orig = 0.001747, loss_refine = 0.541584 [Step 552] loss_orig = 0.001231, loss_refine = -1.616565 Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1168074: cache has only 0 modules 46%|████▌ | 553/1208 [7:22:09<8:01:25, 44.10s/it] {'loss': 0.0032, 'grad_norm': 3.826180924742411, 'learning_rate': 5.422185430463576e-07, 'completion_length': 96.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.0601806640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 3.66} 46%|████▌ | 553/1208 [7:22:09<8:01:25, 44.10s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1168947: cache has only 0 modules Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1169820: cache has only 0 modules 46%|████▌ | 554/1208 [7:22:48<7:45:23, 42.70s/it] {'loss': 0.0016, 'grad_norm': 8.246988071597206, 'learning_rate': 5.413907284768213e-07, 'completion_length': 95.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.04052734375, 'clip_ratio': 0.0, 'epoch': 3.67} 46%|████▌ | 554/1208 [7:22:48<7:45:23, 42.70s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1170693: cache has only 0 modules Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1171566: cache has only 0 modules 46%|████▌ | 555/1208 [7:23:24<7:23:42, 40.77s/it] {'loss': 0.0014, 'grad_norm': 5.731238926791622, 'learning_rate': 5.405629139072847e-07, 'completion_length': 92.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0355224609375, 'clip_ratio': 0.0, 'epoch': 3.68} 46%|████▌ | 555/1208 [7:23:24<7:23:42, 40.77s/it]Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1172439: cache has only 0 modules Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1173312: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'add alarm to the included controls'. Your previous answer was [{'action': 'click', 'coordinate': [1152, 1542]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1174185: cache has only 0 modules [Step 555] loss_orig = 0.001805, loss_refine = 0.001073 [Step 555] loss_orig = 0.001307, loss_refine = 0.001957[Step 555] loss_orig = 0.001192, loss_refine = 0.001344[Step 555] loss_orig = 0.001107, loss_refine = -1.320563 [Step 555] loss_orig = 0.001290, loss_refine = -1.320375[Step 555] loss_orig = 0.001078, loss_refine = 0.001573 [Step 555] loss_orig = 0.000763, loss_refine = 1.324897 [Step 555] loss_orig = 0.000767, loss_refine = 1.324124 46%|████▌ | 556/1208 [7:24:23<8:23:07, 46.30s/it] {'loss': 0.0017, 'grad_norm': 4.259175903270335, 'learning_rate': 5.397350993377483e-07, 'completion_length': 92.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.2519763112068176, 'kl': 0.0341796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 3.68} 46%|████▌ | 556/1208 [7:24:23<8:23:07, 46.30s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1175058: cache has only 0 modules Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1175931: cache has only 0 modules 46%|████▌ | 557/1208 [7:25:03<8:01:43, 44.40s/it] {'loss': 0.0019, 'grad_norm': 5.268700340499738, 'learning_rate': 5.389072847682119e-07, 'completion_length': 83.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.046630859375, 'clip_ratio': 0.0, 'epoch': 3.69} 46%|████▌ | 557/1208 [7:25:03<8:01:43, 44.40s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1176804: cache has only 0 modules Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1177677: cache has only 0 modules 46%|████▌ | 558/1208 [7:25:42<7:40:48, 42.54s/it] {'loss': 0.0017, 'grad_norm': 10.312800079016244, 'learning_rate': 5.380794701986755e-07, 'completion_length': 90.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0418701171875, 'clip_ratio': 0.0, 'epoch': 3.7} 46%|████▌ | 558/1208 [7:25:42<7:40:48, 42.54s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1178550: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1179423: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1180296: cache has only 0 modules [Step 558] loss_orig = 0.001587, loss_refine = -0.351419 [Step 558] loss_orig = 0.001576, loss_refine = -0.351952 [Step 558] loss_orig = 0.001246, loss_refine = -0.351657 [Step 558] loss_orig = 0.001203, loss_refine = -0.351043 [Step 558] loss_orig = 0.001732, loss_refine = -0.350660[Step 558] loss_orig = 0.001916, loss_refine = -0.351177 [Step 558] loss_orig = 0.003531, loss_refine = -0.350335 [Step 558] loss_orig = 0.001086, loss_refine = 2.479396 46%|████▋ | 559/1208 [7:26:31<8:01:31, 44.52s/it] {'loss': 0.0029, 'grad_norm': 7.562126662625739, 'learning_rate': 5.372516556291391e-07, 'completion_length': 80.375, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.060791015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 3.7} 46%|████▋ | 559/1208 [7:26:31<8:01:31, 44.52s/it]Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1181169: cache has only 0 modules Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1182042: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1350, 585]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1182915: cache has only 0 modules [Step 559] loss_orig = 0.001368, loss_refine = -0.838055 [Step 559] loss_orig = 0.001114, loss_refine = 0.506898[Step 559] loss_orig = 0.001100, loss_refine = 0.505635 [Step 559] loss_orig = 0.002347, loss_refine = 0.505779 [Step 559] loss_orig = 0.001123, loss_refine = 0.505458[Step 559] loss_orig = 0.002009, loss_refine = -2.182185 [Step 559] loss_orig = 0.001930, loss_refine = 0.504991 [Step 559] loss_orig = 0.001571, loss_refine = 0.506021 46%|████▋ | 560/1208 [7:27:36<9:06:46, 50.63s/it] {'loss': 0.0014, 'grad_norm': 10.712316281366359, 'learning_rate': 5.364238410596026e-07, 'completion_length': 112.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.24800793329874674, 'kl': 0.03240966796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.71} 46%|████▋ | 560/1208 [7:27:36<9:06:46, 50.63s/it]Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1183788: cache has only 0 modules Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1184661: cache has only 0 modules 46%|████▋ | 561/1208 [7:28:14<8:26:30, 46.97s/it] {'loss': 0.0033, 'grad_norm': 10.507031502759702, 'learning_rate': 5.355960264900661e-07, 'completion_length': 91.1875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.6392731368541718, 'kl': 0.08154296875, 'clip_ratio': 0.0, 'epoch': 3.72} 46%|████▋ | 561/1208 [7:28:14<8:26:30, 46.97s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1185534: cache has only 0 modules Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1186407: cache has only 0 modules 47%|████▋ | 562/1208 [7:28:52<7:56:41, 44.27s/it] {'loss': 0.001, 'grad_norm': 0.3152827260539402, 'learning_rate': 5.347682119205298e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02545166015625, 'clip_ratio': 0.0, 'epoch': 3.72} 47%|████▋ | 562/1208 [7:28:52<7:56:41, 44.27s/it]Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1187280: cache has only 0 modules Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1188153: cache has only 0 modules 47%|████▋ | 563/1208 [7:29:28<7:28:26, 41.71s/it] {'loss': 0.0032, 'grad_norm': 12.9599483239421, 'learning_rate': 5.339403973509934e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.078857421875, 'clip_ratio': 0.0, 'epoch': 3.73} 47%|████▋ | 563/1208 [7:29:28<7:28:26, 41.71s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1189026: cache has only 0 modules Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1189899: cache has only 0 modules 47%|████▋ | 564/1208 [7:30:11<7:32:40, 42.18s/it] {'loss': 0.0031, 'grad_norm': 4.456048195985978, 'learning_rate': 5.331125827814569e-07, 'completion_length': 87.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0772705078125, 'clip_ratio': 0.0, 'epoch': 3.74} 47%|████▋ | 564/1208 [7:30:11<7:32:40, 42.18s/it]Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1190772: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1191645: cache has only 0 modules 47%|████▋ | 565/1208 [7:30:52<7:27:03, 41.72s/it] {'loss': 0.0015, 'grad_norm': 25.954051950873176, 'learning_rate': 5.322847682119204e-07, 'completion_length': 94.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0384521484375, 'clip_ratio': 0.0, 'epoch': 3.74} 47%|████▋ | 565/1208 [7:30:52<7:27:03, 41.72s/it]Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1192518: cache has only 0 modules Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1193391: cache has only 0 modules 47%|████▋ | 566/1208 [7:31:32<7:22:15, 41.33s/it] {'loss': 0.0026, 'grad_norm': 6.134351527180696, 'learning_rate': 5.314569536423841e-07, 'completion_length': 99.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0657958984375, 'clip_ratio': 0.0, 'epoch': 3.75} 47%|████▋ | 566/1208 [7:31:32<7:22:15, 41.33s/it]Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1194264: cache has only 0 modules Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1195137: cache has only 0 modules 47%|████▋ | 567/1208 [7:32:12<7:18:20, 41.03s/it] {'loss': 0.0012, 'grad_norm': 8.294761896336581, 'learning_rate': 5.306291390728477e-07, 'completion_length': 101.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0294189453125, 'clip_ratio': 0.0, 'epoch': 3.75} 47%|████▋ | 567/1208 [7:32:12<7:18:20, 41.03s/it]Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1196010: cache has only 0 modules Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1196883: cache has only 0 modules 47%|████▋ | 568/1208 [7:32:48<6:59:01, 39.28s/it] {'loss': 0.0022, 'grad_norm': 12.868716847029273, 'learning_rate': 5.298013245033112e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0537109375, 'clip_ratio': 0.0, 'epoch': 3.76} 47%|████▋ | 568/1208 [7:32:48<6:59:01, 39.28s/it]Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1197756: cache has only 0 modules Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1198629: cache has only 0 modules 47%|████▋ | 569/1208 [7:33:28<7:03:17, 39.75s/it] {'loss': 0.0038, 'grad_norm': 18.85300851873529, 'learning_rate': 5.289735099337748e-07, 'completion_length': 97.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.095947265625, 'clip_ratio': 0.0, 'epoch': 3.77} 47%|████▋ | 569/1208 [7:33:28<7:03:17, 39.75s/it]Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1199502: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1200375: cache has only 0 modules 47%|████▋ | 570/1208 [7:34:11<7:12:50, 40.71s/it] {'loss': 0.0015, 'grad_norm': 0.3337910854171582, 'learning_rate': 5.281456953642384e-07, 'completion_length': 90.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.036865234375, 'clip_ratio': 0.0, 'epoch': 3.77} 47%|████▋ | 570/1208 [7:34:11<7:12:50, 40.71s/it]Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1201248: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1202121: cache has only 0 modules [Step 570] loss_orig = 0.001215, loss_refine = 0.001201[Step 570] loss_orig = 0.001202, loss_refine = 0.002124 [Step 570] loss_orig = 0.003408, loss_refine = 0.000664[Step 570] loss_orig = 0.000917, loss_refine = 0.004822 [Step 570] loss_orig = 0.001463, loss_refine = 0.001542[Step 570] loss_orig = 0.000556, loss_refine = 0.001057 [Step 570] loss_orig = 0.001755, loss_refine = 0.001544 [Step 570] loss_orig = 0.001250, loss_refine = 0.001189 Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1202994: cache has only 0 modules 47%|████▋ | 571/1208 [7:34:59<7:32:43, 42.64s/it] {'loss': 0.0019, 'grad_norm': 4.047273486418912, 'learning_rate': 5.27317880794702e-07, 'completion_length': 76.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.78} 47%|████▋ | 571/1208 [7:34:59<7:32:43, 42.64s/it]Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1203867: cache has only 0 modules Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1204740: cache has only 0 modules 47%|████▋ | 572/1208 [7:35:37<7:19:13, 41.44s/it] {'loss': 0.0024, 'grad_norm': 16.8487814137529, 'learning_rate': 5.264900662251655e-07, 'completion_length': 93.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0609130859375, 'clip_ratio': 0.0, 'epoch': 3.79} 47%|████▋ | 572/1208 [7:35:37<7:19:13, 41.44s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1205613: cache has only 0 modules Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1206486: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open photo'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1207359: cache has only 0 modules [Step 572] loss_orig = 0.001528, loss_refine = 0.353983[Step 572] loss_orig = 0.001282, loss_refine = -2.472485 [Step 572] loss_orig = 0.001124, loss_refine = 0.355181[Step 572] loss_orig = 0.001567, loss_refine = 0.354681 [Step 572] loss_orig = 0.001531, loss_refine = 0.354979[Step 572] loss_orig = 0.000708, loss_refine = 0.354837 [Step 572] loss_orig = 0.000618, loss_refine = 0.354715[Step 572] loss_orig = 0.001162, loss_refine = 0.354715 47%|████▋ | 573/1208 [7:36:30<7:56:14, 45.00s/it] {'loss': 0.0013, 'grad_norm': 7.958054762320146, 'learning_rate': 5.256622516556292e-07, 'completion_length': 90.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.6666666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.02978515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.79} 47%|████▋ | 573/1208 [7:36:30<7:56:14, 45.00s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1208232: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1209105: cache has only 0 modules 48%|████▊ | 574/1208 [7:37:06<7:26:47, 42.28s/it] {'loss': 0.0019, 'grad_norm': 12.035495622992478, 'learning_rate': 5.248344370860927e-07, 'completion_length': 92.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'epoch': 3.8} 48%|████▊ | 574/1208 [7:37:06<7:26:47, 42.28s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1209978: cache has only 0 modules Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1210851: cache has only 0 modules 48%|████▊ | 575/1208 [7:37:52<7:37:29, 43.36s/it] {'loss': 0.0018, 'grad_norm': 5.841098718042174, 'learning_rate': 5.240066225165562e-07, 'completion_length': 93.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.7659775018692017, 'kl': 0.0460205078125, 'clip_ratio': 0.0, 'epoch': 3.81} 48%|████▊ | 575/1208 [7:37:52<7:37:29, 43.36s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1211724: cache has only 0 modules Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1212597: cache has only 0 modules 48%|████▊ | 576/1208 [7:38:31<7:22:44, 42.03s/it] {'loss': 0.0021, 'grad_norm': 9.641816309633704, 'learning_rate': 5.231788079470198e-07, 'completion_length': 95.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0517578125, 'clip_ratio': 0.0, 'epoch': 3.81} 48%|████▊ | 576/1208 [7:38:31<7:22:44, 42.03s/it]Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1213470: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1214343: cache has only 0 modules [Step 576] loss_orig = 0.001232, loss_refine = -1.206256[Step 576] loss_orig = 0.001220, loss_refine = -1.205811[Step 576] loss_orig = 0.001293, loss_refine = 0.726377 [Step 576] loss_orig = 0.000791, loss_refine = -1.204000 [Step 576] loss_orig = 0.000852, loss_refine = 0.725542 [Step 576] loss_orig = 0.000705, loss_refine = 0.725877[Step 576] loss_orig = 0.000958, loss_refine = 0.725837 [Step 576] loss_orig = 0.000948, loss_refine = 0.726732 Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1215216: cache has only 0 modules 48%|████▊ | 577/1208 [7:39:17<7:32:36, 43.04s/it] {'loss': 0.0013, 'grad_norm': 11.915701248205977, 'learning_rate': 5.223509933774835e-07, 'completion_length': 85.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.3268197377522786, 'kl': 0.022705078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.82} 48%|████▊ | 577/1208 [7:39:17<7:32:36, 43.04s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1216089: cache has only 0 modules Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1216962: cache has only 0 modules 48%|████▊ | 578/1208 [7:39:58<7:27:57, 42.66s/it] {'loss': 0.0029, 'grad_norm': 5.44432094234835, 'learning_rate': 5.21523178807947e-07, 'completion_length': 90.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.072265625, 'clip_ratio': 0.0, 'epoch': 3.83} 48%|████▊ | 578/1208 [7:39:58<7:27:57, 42.66s/it]Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1217835: cache has only 0 modules Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1218708: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1219581: cache has only 0 modules [Step 578] loss_orig = 0.002571, loss_refine = -0.722705[Step 578] loss_orig = 0.002374, loss_refine = 0.249328 [Step 578] loss_orig = 0.001488, loss_refine = -0.722443 [Step 578] loss_orig = 0.002080, loss_refine = 0.245670 [Step 578] loss_orig = 0.001535, loss_refine = -0.718699 [Step 578] loss_orig = 0.001454, loss_refine = -0.722220[Step 578] loss_orig = 0.003445, loss_refine = 0.244600 [Step 578] loss_orig = 0.001807, loss_refine = 2.175547 48%|████▊ | 579/1208 [7:41:08<8:51:40, 50.72s/it] {'loss': 0.0025, 'grad_norm': 2.956111103264465, 'learning_rate': 5.206953642384105e-07, 'completion_length': 107.41666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4166666666666665, 'reward_std': 0.3450327714284261, 'kl': 0.0435791015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 3.83} 48%|████▊ | 579/1208 [7:41:08<8:51:40, 50.72s/it]Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1220454: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1221327: cache has only 0 modules 48%|████▊ | 580/1208 [7:41:47<8:15:01, 47.30s/it] {'loss': 0.0017, 'grad_norm': 9.885540570420739, 'learning_rate': 5.198675496688742e-07, 'completion_length': 105.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.04278564453125, 'clip_ratio': 0.0, 'epoch': 3.84} 48%|████▊ | 580/1208 [7:41:47<8:15:01, 47.30s/it]Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1222200: cache has only 0 modules Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1223073: cache has only 0 modules 48%|████▊ | 581/1208 [7:42:22<7:34:02, 43.45s/it] {'loss': 0.0021, 'grad_norm': 5.266866731080643, 'learning_rate': 5.190397350993378e-07, 'completion_length': 88.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.05340576171875, 'clip_ratio': 0.0, 'epoch': 3.85} 48%|████▊ | 581/1208 [7:42:22<7:34:02, 43.45s/it]Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1223946: cache has only 0 modules Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1224819: cache has only 0 modules 48%|████▊ | 582/1208 [7:42:59<7:14:57, 41.69s/it] {'loss': 0.0013, 'grad_norm': 4.286836989595358, 'learning_rate': 5.182119205298013e-07, 'completion_length': 95.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.032958984375, 'clip_ratio': 0.0, 'epoch': 3.85} 48%|████▊ | 582/1208 [7:42:59<7:14:57, 41.69s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1225692: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1226565: cache has only 0 modules 48%|████▊ | 583/1208 [7:43:34<6:51:10, 39.47s/it] {'loss': 0.0016, 'grad_norm': 2.069600564835229, 'learning_rate': 5.173841059602648e-07, 'completion_length': 87.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03924560546875, 'clip_ratio': 0.0, 'epoch': 3.86} 48%|████▊ | 583/1208 [7:43:34<6:51:10, 39.47s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1227438: cache has only 0 modules Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1228311: cache has only 0 modules 48%|████▊ | 584/1208 [7:44:13<6:50:31, 39.47s/it] {'loss': 0.0014, 'grad_norm': 3.5402334300813134, 'learning_rate': 5.165562913907285e-07, 'completion_length': 86.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03485107421875, 'clip_ratio': 0.0, 'epoch': 3.87} 48%|████▊ | 584/1208 [7:44:13<6:50:31, 39.47s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1229184: cache has only 0 modules Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1230057: cache has only 0 modules 48%|████▊ | 585/1208 [7:44:53<6:49:45, 39.46s/it] {'loss': 0.0039, 'grad_norm': 4.77850882793739, 'learning_rate': 5.15728476821192e-07, 'completion_length': 99.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.096435546875, 'clip_ratio': 0.0, 'epoch': 3.87} 48%|████▊ | 585/1208 [7:44:53<6:49:45, 39.46s/it]Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1230930: cache has only 0 modules Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1231803: cache has only 0 modules 49%|████▊ | 586/1208 [7:45:31<6:45:41, 39.13s/it] {'loss': 0.0016, 'grad_norm': 0.3314473606636084, 'learning_rate': 5.149006622516556e-07, 'completion_length': 95.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04071044921875, 'clip_ratio': 0.0, 'epoch': 3.88} 49%|████▊ | 586/1208 [7:45:31<6:45:41, 39.13s/it]Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1232676: cache has only 0 modules Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1233549: cache has only 0 modules 49%|████▊ | 587/1208 [7:46:09<6:42:41, 38.91s/it] {'loss': 0.0032, 'grad_norm': 3.8699445429092076, 'learning_rate': 5.140728476821192e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.078857421875, 'clip_ratio': 0.0, 'epoch': 3.89} 49%|████▊ | 587/1208 [7:46:09<6:42:41, 38.91s/it]Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1234422: cache has only 0 modules Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1235295: cache has only 0 modules 49%|████▊ | 588/1208 [7:46:53<6:55:58, 40.26s/it] {'loss': 0.0024, 'grad_norm': 7.438487031227734, 'learning_rate': 5.132450331125828e-07, 'completion_length': 87.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.059814453125, 'clip_ratio': 0.0, 'epoch': 3.89} 49%|████▊ | 588/1208 [7:46:53<6:55:58, 40.26s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1236168: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1237041: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [470, 447]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1237914: cache has only 0 modules [Step 588] loss_orig = 0.002122, loss_refine = -0.350966[Step 588] loss_orig = 0.001539, loss_refine = -0.351238 [Step 588] loss_orig = 0.001025, loss_refine = -0.350569 [Step 588] loss_orig = 0.002508, loss_refine = 2.480468 [Step 588] loss_orig = 0.001309, loss_refine = -0.351121 [Step 588] loss_orig = 0.002126, loss_refine = -0.350872 [Step 588] loss_orig = 0.002464, loss_refine = -0.351635 [Step 588] loss_orig = 0.001410, loss_refine = -0.351062 49%|████▉ | 589/1208 [7:47:56<8:05:13, 47.03s/it] {'loss': 0.0024, 'grad_norm': 13.659449111915901, 'learning_rate': 5.124172185430463e-07, 'completion_length': 100.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.9} 49%|████▉ | 589/1208 [7:47:56<8:05:13, 47.03s/it]Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1238787: cache has only 0 modules Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1239660: cache has only 0 modules 49%|████▉ | 590/1208 [7:48:32<7:32:20, 43.92s/it] {'loss': 0.0018, 'grad_norm': 0.31613193188057537, 'learning_rate': 5.115894039735099e-07, 'completion_length': 84.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0445556640625, 'clip_ratio': 0.0, 'epoch': 3.91} 49%|████▉ | 590/1208 [7:48:32<7:32:20, 43.92s/it]Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1240533: cache has only 0 modules Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1241406: cache has only 0 modules 49%|████▉ | 591/1208 [7:49:10<7:13:21, 42.14s/it] {'loss': 0.0012, 'grad_norm': 6.034337908203862, 'learning_rate': 5.107615894039736e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.031005859375, 'clip_ratio': 0.0, 'epoch': 3.91} 49%|████▉ | 591/1208 [7:49:10<7:13:21, 42.14s/it]Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1242279: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1243152: cache has only 0 modules [Step 591] loss_orig = 0.001211, loss_refine = -1.616898 [Step 591] loss_orig = 0.000798, loss_refine = 0.542108[Step 591] loss_orig = 0.003666, loss_refine = 0.541926 [Step 591] loss_orig = 0.001031, loss_refine = 0.541182[Step 591] loss_orig = 0.001312, loss_refine = 0.541161[Step 591] loss_orig = 0.000986, loss_refine = 0.541180 [Step 591] loss_orig = 0.001346, loss_refine = 0.543008 [Step 591] loss_orig = 0.001756, loss_refine = -1.618086 Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1244025: cache has only 0 modules 49%|████▉ | 592/1208 [7:49:58<7:31:32, 43.98s/it] {'loss': 0.002, 'grad_norm': 41.747311484337466, 'learning_rate': 5.099337748344371e-07, 'completion_length': 91.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.3268197377522786, 'kl': 0.0439453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 3.92} 49%|████▉ | 592/1208 [7:49:58<7:31:32, 43.98s/it]Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1244898: cache has only 0 modules Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1245771: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1246644: cache has only 0 modules [Step 592] loss_orig = 0.002080, loss_refine = -1.617155 [Step 592] loss_orig = 0.001342, loss_refine = 0.542108 [Step 592] loss_orig = 0.002650, loss_refine = 0.542006 [Step 592] loss_orig = 0.001035, loss_refine = -1.617423 [Step 592] loss_orig = 0.003103, loss_refine = 0.542605[Step 592] loss_orig = 0.002977, loss_refine = 0.543281 [Step 592] loss_orig = 0.001760, loss_refine = 0.542031 [Step 592] loss_orig = 0.001373, loss_refine = 0.544045 49%|████▉ | 593/1208 [7:50:52<8:01:11, 46.95s/it] {'loss': 0.002, 'grad_norm': 4.480756642005226, 'learning_rate': 5.091059602649006e-07, 'completion_length': 91.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.04248046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.93} 49%|████▉ | 593/1208 [7:50:52<8:01:11, 46.95s/it]Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1247517: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - System'. Your previous answer was [{'action': 'click', 'coordinate': [2292, 21]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box diff coord reward error closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1248390: cache has only 0 modules [Step 593] loss_orig = 0.002428, loss_refine = -1.847018[Step 593] loss_orig = 0.003126, loss_refine = -0.502441[Step 593] loss_orig = 0.002697, loss_refine = 0.841215 [Step 593] loss_orig = 0.002228, loss_refine = 0.840853[Step 593] loss_orig = 0.001801, loss_refine = -0.502808 [Step 593] loss_orig = 0.001780, loss_refine = 0.841276[Step 593] loss_orig = 0.001945, loss_refine = 0.841496 [Step 593] loss_orig = 0.001636, loss_refine = -0.500394 Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1249263: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display ip address'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1250136: cache has only 0 modules [Step 593] loss_orig = 0.002071, loss_refine = -0.538736[Step 593] loss_orig = 0.002817, loss_refine = -0.534918[Step 593] loss_orig = 0.001966, loss_refine = -0.536827 [Step 593] loss_orig = 0.004109, loss_refine = -0.537441 [Step 593] loss_orig = 0.001833, loss_refine = -0.537278 [Step 593] loss_orig = 0.002125, loss_refine = 1.622348 [Step 593] loss_orig = 0.002139, loss_refine = -0.536159 [Step 593] loss_orig = 0.004263, loss_refine = 1.622769 49%|████▉ | 594/1208 [7:51:57<8:56:09, 52.39s/it] {'loss': 0.0022, 'grad_norm': 6.276319099326455, 'learning_rate': 5.082781456953642e-07, 'completion_length': 89.09375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 1.0, 'reward': 2.34375, 'reward_std': 0.30173346400260925, 'kl': 0.060791015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 3.93} 49%|████▉ | 594/1208 [7:51:57<8:56:09, 52.39s/it]Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1251009: cache has only 0 modules Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1251882: cache has only 0 modules 49%|████▉ | 595/1208 [7:52:35<8:09:43, 47.93s/it] {'loss': 0.0011, 'grad_norm': 0.18865846422562246, 'learning_rate': 5.074503311258278e-07, 'completion_length': 90.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0281982421875, 'clip_ratio': 0.0, 'epoch': 3.94} 49%|████▉ | 595/1208 [7:52:35<8:09:43, 47.93s/it]Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1252755: cache has only 0 modules Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1253628: cache has only 0 modules 49%|████▉ | 596/1208 [7:53:09<7:27:21, 43.86s/it] {'loss': 0.0015, 'grad_norm': 9.468072128128325, 'learning_rate': 5.066225165562914e-07, 'completion_length': 80.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.037109375, 'clip_ratio': 0.0, 'epoch': 3.95} 49%|████▉ | 596/1208 [7:53:09<7:27:21, 43.86s/it]Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1254501: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1255374: cache has only 0 modules 49%|████▉ | 597/1208 [7:53:43<6:54:32, 40.71s/it] {'loss': 0.0013, 'grad_norm': 13.50028055305999, 'learning_rate': 5.057947019867549e-07, 'completion_length': 85.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03271484375, 'clip_ratio': 0.0, 'epoch': 3.95} 49%|████▉ | 597/1208 [7:53:43<6:54:32, 40.71s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1256247: cache has only 0 modules Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1257120: cache has only 0 modules 50%|████▉ | 598/1208 [7:54:16<6:30:25, 38.40s/it] {'loss': 0.0019, 'grad_norm': 20.879977081503466, 'learning_rate': 5.049668874172185e-07, 'completion_length': 83.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.04815673828125, 'clip_ratio': 0.0, 'epoch': 3.96} 50%|████▉ | 598/1208 [7:54:16<6:30:25, 38.40s/it]Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1257993: cache has only 0 modules Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1258866: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'use airplay'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1259739: cache has only 0 modules [Step 598] loss_orig = -0.351905, loss_refine = 0.507454[Step 598] loss_orig = 2.476082, loss_refine = -2.179663[Step 598] loss_orig = -0.351436, loss_refine = 0.505176[Step 598] loss_orig = -0.351072, loss_refine = -0.838111 [Step 598] loss_orig = -0.351153, loss_refine = 0.504751[Step 598] loss_orig = -0.351895, loss_refine = 0.505546 [Step 598] loss_orig = -0.352521, loss_refine = 0.505542 [Step 598] loss_orig = -0.349433, loss_refine = 0.509010 50%|████▉ | 599/1208 [7:55:07<7:09:52, 42.35s/it] {'loss': 0.0022, 'grad_norm': 6.91184298683987, 'learning_rate': 5.041390728476821e-07, 'completion_length': 88.5, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.2916666666666665, 'reward_std': 0.6380135416984558, 'kl': 0.05126953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 3.97} 50%|████▉ | 599/1208 [7:55:07<7:09:52, 42.35s/it]Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1260612: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1261485: cache has only 0 modules [Step 599] loss_orig = 0.003007, loss_refine = 0.727419[Step 599] loss_orig = 0.000734, loss_refine = -1.204174[Step 599] loss_orig = 0.001743, loss_refine = 0.727417[Step 599] loss_orig = 0.003309, loss_refine = 0.726190 [Step 599] loss_orig = 0.001371, loss_refine = 0.725450 [Step 599] loss_orig = 0.000832, loss_refine = -1.205675 [Step 599] loss_orig = 0.001541, loss_refine = 0.734086 [Step 599] loss_orig = 0.008621, loss_refine = -1.201711 Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1262358: cache has only 0 modules 50%|████▉ | 600/1208 [7:56:08<8:05:03, 47.87s/it] {'loss': 0.0026, 'grad_norm': 9.431719225532818, 'learning_rate': 5.033112582781457e-07, 'completion_length': 90.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.3268197377522786, 'kl': 0.052978515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 3.97} 50%|████▉ | 600/1208 [7:56:08<8:05:03, 47.87s/it]Start loss calc for inst: click the UI element Accessibility Menu /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1263231: cache has only 0 modules Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1264104: cache has only 0 modules 50%|████▉ | 601/1208 [7:57:01<8:20:12, 49.44s/it] {'loss': 0.0015, 'grad_norm': 0.32897977038025933, 'learning_rate': 5.024834437086093e-07, 'completion_length': 84.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03680419921875, 'clip_ratio': 0.0, 'epoch': 3.98} 50%|████▉ | 601/1208 [7:57:01<8:20:12, 49.44s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1264977: cache has only 0 modules Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1265850: cache has only 0 modules 50%|████▉ | 602/1208 [7:57:33<7:25:41, 44.13s/it] {'loss': 0.0016, 'grad_norm': 5.255128657174663, 'learning_rate': 5.016556291390727e-07, 'completion_length': 77.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03955078125, 'clip_ratio': 0.0, 'epoch': 3.99} 50%|████▉ | 602/1208 [7:57:33<7:25:41, 44.13s/it]Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1266723: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1267596: cache has only 0 modules 50%|████▉ | 603/1208 [7:58:08<6:58:21, 41.49s/it] {'loss': 0.0011, 'grad_norm': 0.20635939076483575, 'learning_rate': 5.008278145695364e-07, 'completion_length': 87.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02655029296875, 'clip_ratio': 0.0, 'epoch': 3.99} 50%|████▉ | 603/1208 [7:58:08<6:58:21, 41.49s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1268469: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.6666666865348816 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1269342: cache has only 0 modules [Step 603] loss_orig = 0.002600, loss_refine = -0.537248[Step 603] loss_orig = 0.002696, loss_refine = -0.536983[Step 603] loss_orig = 0.001385, loss_refine = 1.621810[Step 603] loss_orig = 0.000922, loss_refine = -0.538108[Step 603] loss_orig = 0.003577, loss_refine = -0.537615[Step 603] loss_orig = 0.002444, loss_refine = -0.538823 [Step 603] loss_orig = 0.002988, loss_refine = -0.535536 [Step 603] loss_orig = 0.001626, loss_refine = 1.622599 Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.1666666716337204 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1270215: cache has only 0 modules 50%|█████ | 604/1208 [7:58:56<7:15:32, 43.27s/it] {'loss': 0.0023, 'grad_norm': 16.164189965476357, 'learning_rate': 5e-07, 'completion_length': 86.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.055555557211240135, 'rewards/format_reward': 1.0, 'reward': 2.277777830759684, 'reward_std': 0.30860670407613117, 'kl': 0.0552978515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.6666666865348816, 'epoch': 4.0} 50%|█████ | 604/1208 [7:58:56<7:15:32, 43.27s/it]Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1271088: cache has only 0 modules Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1271961: cache has only 0 modules 50%|█████ | 605/1208 [7:59:34<7:00:13, 41.81s/it] {'loss': 0.0021, 'grad_norm': 6.805635325260517, 'learning_rate': 4.991721854304635e-07, 'completion_length': 90.375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.3204349875450134, 'kl': 0.0518798828125, 'clip_ratio': 0.0, 'epoch': 4.01} 50%|█████ | 605/1208 [7:59:34<7:00:13, 41.81s/it]Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1272834: cache has only 0 modules Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1273707: cache has only 0 modules 50%|█████ | 606/1208 [8:00:15<6:56:25, 41.50s/it] {'loss': 0.0014, 'grad_norm': 7.310534619735861, 'learning_rate': 4.983443708609271e-07, 'completion_length': 86.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0350341796875, 'clip_ratio': 0.0, 'epoch': 4.01} 50%|█████ | 606/1208 [8:00:15<6:56:25, 41.50s/it]Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1274580: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1275453: cache has only 0 modules [Step 606] loss_orig = 0.001314, loss_refine = 0.938243 [Step 606] loss_orig = 0.002762, loss_refine = 0.937268 [Step 606] loss_orig = 0.002819, loss_refine = -0.933852[Step 606] loss_orig = 0.002280, loss_refine = 0.937068 [Step 606] loss_orig = 0.001698, loss_refine = -0.933795 [Step 606] loss_orig = 0.001739, loss_refine = -0.933124 [Step 606] loss_orig = 0.001551, loss_refine = 0.942602[Step 606] loss_orig = 0.001492, loss_refine = -0.933551 Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1276326: cache has only 0 modules 50%|█████ | 607/1208 [8:01:16<7:53:43, 47.29s/it] {'loss': 0.0064, 'grad_norm': 24.27643299228298, 'learning_rate': 4.975165562913907e-07, 'completion_length': 100.95833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.2960252861181895, 'kl': 0.1513671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 4.02} 50%|█████ | 607/1208 [8:01:16<7:53:43, 47.29s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1277199: cache has only 0 modules Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1278072: cache has only 0 modules 50%|█████ | 608/1208 [8:01:49<7:10:30, 43.05s/it] {'loss': 0.0016, 'grad_norm': 8.682350915044905, 'learning_rate': 4.966887417218543e-07, 'completion_length': 79.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.038818359375, 'clip_ratio': 0.0, 'epoch': 4.03} 50%|█████ | 608/1208 [8:01:49<7:10:30, 43.05s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1278945: cache has only 0 modules Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1279818: cache has only 0 modules 50%|█████ | 609/1208 [8:02:22<6:42:00, 40.27s/it] {'loss': 0.0013, 'grad_norm': 0.2984323623333093, 'learning_rate': 4.958609271523178e-07, 'completion_length': 81.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.031494140625, 'clip_ratio': 0.0, 'epoch': 4.03} 50%|█████ | 609/1208 [8:02:22<6:42:00, 40.27s/it]Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1280691: cache has only 0 modules Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1281564: cache has only 0 modules 50%|█████ | 610/1208 [8:02:58<6:28:00, 38.93s/it] {'loss': 0.0026, 'grad_norm': 0.5635394725427499, 'learning_rate': 4.950331125827814e-07, 'completion_length': 87.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0653076171875, 'clip_ratio': 0.0, 'epoch': 4.04} 50%|█████ | 610/1208 [8:02:58<6:28:00, 38.93s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1282437: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box diff coord reward errorcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1283310: cache has only 0 modules [Step 610] loss_orig = 0.003842, loss_refine = 0.196984[Step 610] loss_orig = 0.000919, loss_refine = -1.362202[Step 610] loss_orig = 0.001005, loss_refine = 0.197159 [Step 610] loss_orig = 0.001093, loss_refine = 0.197581 [Step 610] loss_orig = 0.003614, loss_refine = 0.197295 [Step 610] loss_orig = 0.002163, loss_refine = 0.204177 [Step 610] loss_orig = 0.001905, loss_refine = 1.758564 [Step 610] loss_orig = 0.001208, loss_refine = -1.364294 Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1284183: cache has only 0 modules 51%|█████ | 611/1208 [8:03:58<7:28:38, 45.09s/it] {'loss': 0.0025, 'grad_norm': 5.419885985316925, 'learning_rate': 4.94205298013245e-07, 'completion_length': 91.08333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.21362332503000894, 'kl': 0.04833984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.05} 51%|█████ | 611/1208 [8:03:58<7:28:38, 45.09s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1285056: cache has only 0 modules Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1285929: cache has only 0 modules 51%|█████ | 612/1208 [8:04:35<7:04:23, 42.72s/it] {'loss': 0.0035, 'grad_norm': 6.650883270465139, 'learning_rate': 4.933774834437086e-07, 'completion_length': 89.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0885009765625, 'clip_ratio': 0.0, 'epoch': 4.05} 51%|█████ | 612/1208 [8:04:35<7:04:23, 42.72s/it]Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1286802: cache has only 0 modules Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1287675: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1288548: cache has only 0 modules [Step 612] loss_orig = 0.001402, loss_refine = 0.937251[Step 612] loss_orig = 0.000925, loss_refine = -0.933553[Step 612] loss_orig = 0.001277, loss_refine = 0.936959 [Step 612] loss_orig = 0.001083, loss_refine = 0.937776 [Step 612] loss_orig = 0.000849, loss_refine = 0.938385 [Step 612] loss_orig = 0.002436, loss_refine = -0.933565 [Step 612] loss_orig = 0.002146, loss_refine = -0.933322 [Step 612] loss_orig = 0.001809, loss_refine = -0.933319 51%|█████ | 613/1208 [8:05:32<7:45:03, 46.90s/it] {'loss': 0.0027, 'grad_norm': 13.259736662527587, 'learning_rate': 4.925496688741721e-07, 'completion_length': 93.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.33247750997543335, 'kl': 0.0614013671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 4.06} 51%|█████ | 613/1208 [8:05:32<7:45:03, 46.90s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1289421: cache has only 0 modules Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1290294: cache has only 0 modules 51%|█████ | 614/1208 [8:06:11<7:23:18, 44.78s/it] {'loss': 0.0017, 'grad_norm': 4.017556995371222, 'learning_rate': 4.917218543046358e-07, 'completion_length': 86.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0419921875, 'clip_ratio': 0.0, 'epoch': 4.07} 51%|█████ | 614/1208 [8:06:11<7:23:18, 44.78s/it]Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1291167: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1292040: cache has only 0 modules [Step 614] loss_orig = 0.001112, loss_refine = -1.612005 [Step 614] loss_orig = 0.001932, loss_refine = 0.541985 [Step 614] loss_orig = 0.001979, loss_refine = 0.542702[Step 614] loss_orig = 0.001132, loss_refine = 0.540936 [Step 614] loss_orig = 0.000637, loss_refine = -1.617296[Step 614] loss_orig = 0.001637, loss_refine = 0.542602 [Step 614] loss_orig = 0.002691, loss_refine = 0.542050 [Step 614] loss_orig = 0.001263, loss_refine = 0.542683 Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1292913: cache has only 0 modules 51%|█████ | 615/1208 [8:07:02<7:40:45, 46.62s/it] {'loss': 0.0022, 'grad_norm': 12.58864570054975, 'learning_rate': 4.908940397350992e-07, 'completion_length': 84.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.2083333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.0379638671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.07} 51%|█████ | 615/1208 [8:07:02<7:40:45, 46.62s/it]Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1293786: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Decorative Locked'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1294659: cache has only 0 modules [Step 615] loss_orig = 0.002584, loss_refine = -0.351523[Step 615] loss_orig = 0.001963, loss_refine = -0.346008 [Step 615] loss_orig = 0.001436, loss_refine = -0.348820[Step 615] loss_orig = 0.001808, loss_refine = -0.350856 [Step 615] loss_orig = 0.001321, loss_refine = -0.352166[Step 615] loss_orig = 0.002106, loss_refine = -0.351770 [Step 615] loss_orig = 0.000864, loss_refine = -0.351324[Step 615] loss_orig = 0.004582, loss_refine = 2.476368 Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1295532: cache has only 0 modules 51%|█████ | 616/1208 [8:07:58<8:05:18, 49.19s/it] {'loss': 0.0022, 'grad_norm': 6.216493274197843, 'learning_rate': 4.900662251655629e-07, 'completion_length': 95.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.04296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 4.08} 51%|█████ | 616/1208 [8:07:58<8:05:18, 49.19s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1296405: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1297278: cache has only 0 modules 51%|█████ | 617/1208 [8:08:34<7:27:58, 45.48s/it] {'loss': 0.0009, 'grad_norm': 13.38502374547299, 'learning_rate': 4.892384105960264e-07, 'completion_length': 88.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02276611328125, 'clip_ratio': 0.0, 'epoch': 4.09} 51%|█████ | 617/1208 [8:08:34<7:27:58, 45.48s/it]Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1298151: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1299024: cache has only 0 modules 51%|█████ | 618/1208 [8:09:13<7:07:25, 43.47s/it] {'loss': 0.0025, 'grad_norm': 4.837677707770491, 'learning_rate': 4.884105960264901e-07, 'completion_length': 97.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0635986328125, 'clip_ratio': 0.0, 'epoch': 4.09} 51%|█████ | 618/1208 [8:09:13<7:07:25, 43.47s/it]Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1299897: cache has only 0 modules Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1300770: cache has only 0 modules 51%|█████ | 619/1208 [8:09:48<6:40:54, 40.84s/it] {'loss': 0.0018, 'grad_norm': 4.463533303431657, 'learning_rate': 4.875827814569536e-07, 'completion_length': 85.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0452880859375, 'clip_ratio': 0.0, 'epoch': 4.1} 51%|█████ | 619/1208 [8:09:48<6:40:54, 40.84s/it]Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1301643: cache has only 0 modules Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1302516: cache has only 0 modules 51%|█████▏ | 620/1208 [8:10:25<6:29:33, 39.75s/it] {'loss': 0.0013, 'grad_norm': 5.283296062056635, 'learning_rate': 4.867549668874172e-07, 'completion_length': 89.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.03277587890625, 'clip_ratio': 0.0, 'epoch': 4.11} 51%|█████▏ | 620/1208 [8:10:25<6:29:33, 39.75s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1303389: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1304262: cache has only 0 modules 51%|█████▏ | 621/1208 [8:11:05<6:29:38, 39.83s/it] {'loss': 0.0051, 'grad_norm': 8.91988400294341, 'learning_rate': 4.859271523178808e-07, 'completion_length': 85.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.12744140625, 'clip_ratio': 0.0, 'epoch': 4.11} 51%|█████▏ | 621/1208 [8:11:05<6:29:38, 39.83s/it]Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1305135: cache has only 0 modules Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1306008: cache has only 0 modules 51%|█████▏ | 622/1208 [8:11:47<6:33:57, 40.34s/it] {'loss': 0.0014, 'grad_norm': 5.471665179277213, 'learning_rate': 4.850993377483443e-07, 'completion_length': 105.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.033935546875, 'clip_ratio': 0.0, 'epoch': 4.12} 51%|█████▏ | 622/1208 [8:11:47<6:33:57, 40.34s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1306881: cache has only 0 modules Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1307754: cache has only 0 modules 52%|█████▏ | 623/1208 [8:12:20<6:14:20, 38.39s/it] {'loss': 0.0019, 'grad_norm': 14.33720211740526, 'learning_rate': 4.842715231788079e-07, 'completion_length': 74.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.0478515625, 'clip_ratio': 0.0, 'epoch': 4.13} 52%|█████▏ | 623/1208 [8:12:20<6:14:20, 38.39s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1308627: cache has only 0 modules Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1309500: cache has only 0 modules 52%|█████▏ | 624/1208 [8:12:59<6:13:31, 38.38s/it] {'loss': 0.0015, 'grad_norm': 0.2664723325056382, 'learning_rate': 4.834437086092715e-07, 'completion_length': 90.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.037841796875, 'clip_ratio': 0.0, 'epoch': 4.13} 52%|█████▏ | 624/1208 [8:12:59<6:13:31, 38.38s/it]Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1310373: cache has only 0 modules Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1311246: cache has only 0 modules 52%|█████▏ | 625/1208 [8:13:32<5:58:15, 36.87s/it] {'loss': 0.0022, 'grad_norm': 5.016979721684994, 'learning_rate': 4.826158940397351e-07, 'completion_length': 84.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0557861328125, 'clip_ratio': 0.0, 'epoch': 4.14} 52%|█████▏ | 625/1208 [8:13:32<5:58:15, 36.87s/it]Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1312119: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command '1'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1312992: cache has only 0 modules [Step 625] loss_orig = 0.003813, loss_refine = 0.937158[Step 625] loss_orig = 0.001256, loss_refine = 0.938554 [Step 625] loss_orig = 0.000934, loss_refine = -0.933911 [Step 625] loss_orig = 0.000711, loss_refine = -0.933797[Step 625] loss_orig = 0.001511, loss_refine = -0.934112 [Step 625] loss_orig = 0.001534, loss_refine = 0.937184 [Step 625] loss_orig = 0.001448, loss_refine = 0.936959 [Step 625] loss_orig = 0.000884, loss_refine = -0.933890 Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1313865: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [831, 89]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1314738: cache has only 0 modules [Step 625] loss_orig = 0.001400, loss_refine = 0.542312 [Step 625] loss_orig = 0.002222, loss_refine = 0.540929[Step 625] loss_orig = 0.002131, loss_refine = 0.541767[Step 625] loss_orig = 0.000998, loss_refine = 0.542033[Step 625] loss_orig = 0.002464, loss_refine = 0.541496[Step 625] loss_orig = 0.001315, loss_refine = -1.618356 [Step 625] loss_orig = 0.001192, loss_refine = -1.618040 [Step 625] loss_orig = 0.001934, loss_refine = 0.541991 52%|█████▏ | 626/1208 [8:14:39<7:25:29, 45.93s/it] {'loss': 0.0018, 'grad_norm': 6.9919623399727175, 'learning_rate': 4.817880794701986e-07, 'completion_length': 100.65625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.249358132481575, 'kl': 0.040283203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 4.15} 52%|█████▏ | 626/1208 [8:14:39<7:25:29, 45.93s/it]Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1315611: cache has only 0 modules Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1316484: cache has only 0 modules 52%|█████▏ | 627/1208 [8:15:17<7:02:21, 43.62s/it] {'loss': 0.0018, 'grad_norm': 15.532728205272866, 'learning_rate': 4.809602649006622e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.043701171875, 'clip_ratio': 0.0, 'epoch': 4.15} 52%|█████▏ | 627/1208 [8:15:17<7:02:21, 43.62s/it]Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1317357: cache has only 0 modules Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1318230: cache has only 0 modules 52%|█████▏ | 628/1208 [8:15:57<6:49:47, 42.39s/it] {'loss': 0.0043, 'grad_norm': 10.172701306205507, 'learning_rate': 4.801324503311258e-07, 'completion_length': 86.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.107421875, 'clip_ratio': 0.0, 'epoch': 4.16} 52%|█████▏ | 628/1208 [8:15:57<6:49:47, 42.39s/it]Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1319103: cache has only 0 modules Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1319976: cache has only 0 modules 52%|█████▏ | 629/1208 [8:16:37<6:40:55, 41.55s/it] {'loss': 0.0026, 'grad_norm': 9.37567871758297, 'learning_rate': 4.793046357615893e-07, 'completion_length': 81.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.06451416015625, 'clip_ratio': 0.0, 'epoch': 4.17} 52%|█████▏ | 629/1208 [8:16:37<6:40:55, 41.55s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1320849: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1321722: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1322595: cache has only 0 modules [Step 629] loss_orig = 0.005076, loss_refine = 0.001400[Step 629] loss_orig = 0.002590, loss_refine = 0.002108 [Step 629] loss_orig = 0.000426, loss_refine = 0.001098 [Step 629] loss_orig = 0.001255, loss_refine = 0.001604[Step 629] loss_orig = 0.001386, loss_refine = 0.001460 [Step 629] loss_orig = 0.002305, loss_refine = 0.002339 [Step 629] loss_orig = 0.001812, loss_refine = 0.002145 [Step 629] loss_orig = 0.002684, loss_refine = 0.001261 52%|█████▏ | 630/1208 [8:17:35<7:30:07, 46.73s/it] {'loss': 0.0012, 'grad_norm': 0.25108551864706546, 'learning_rate': 4.78476821192053e-07, 'completion_length': 88.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.03759765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 4.17} 52%|█████▏ | 630/1208 [8:17:35<7:30:07, 46.73s/it]Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1323468: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1324341: cache has only 0 modules 52%|█████▏ | 631/1208 [8:18:15<7:08:10, 44.52s/it] {'loss': 0.0018, 'grad_norm': 0.3713853042142241, 'learning_rate': 4.776490066225166e-07, 'completion_length': 93.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'epoch': 4.18} 52%|█████▏ | 631/1208 [8:18:15<7:08:10, 44.52s/it]Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1325214: cache has only 0 modules Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1326087: cache has only 0 modules 52%|█████▏ | 632/1208 [8:18:56<6:57:25, 43.48s/it] {'loss': 0.0015, 'grad_norm': 14.707370188386832, 'learning_rate': 4.768211920529801e-07, 'completion_length': 97.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.03729248046875, 'clip_ratio': 0.0, 'epoch': 4.19} 52%|█████▏ | 632/1208 [8:18:56<6:57:25, 43.48s/it]Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1326960: cache has only 0 modules Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1327833: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Font Name'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1328706: cache has only 0 modules [Step 632] loss_orig = 0.002501, loss_refine = -0.720830[Step 632] loss_orig = 0.002242, loss_refine = -0.722239[Step 632] loss_orig = 0.001275, loss_refine = 1.209566 [Step 632] loss_orig = 0.002488, loss_refine = 1.209574[Step 632] loss_orig = 0.001778, loss_refine = -0.723455 [Step 632] loss_orig = 0.001963, loss_refine = 1.208976 [Step 632] loss_orig = 0.001234, loss_refine = -0.720915 [Step 632] loss_orig = 0.001312, loss_refine = -0.722213 52%|█████▏ | 633/1208 [8:19:51<7:29:51, 46.94s/it] {'loss': 0.0022, 'grad_norm': 53.90371221867888, 'learning_rate': 4.759933774834437e-07, 'completion_length': 103.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.3268197377522786, 'kl': 0.0496826171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 4.19} 52%|█████▏ | 633/1208 [8:19:51<7:29:51, 46.94s/it]Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1329579: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1330452: cache has only 0 modules 52%|█████▏ | 634/1208 [8:20:31<7:10:24, 44.99s/it] {'loss': 0.0025, 'grad_norm': 13.301012095585426, 'learning_rate': 4.7516556291390724e-07, 'completion_length': 93.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.0635986328125, 'clip_ratio': 0.0, 'epoch': 4.2} 52%|█████▏ | 634/1208 [8:20:31<7:10:24, 44.99s/it]Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1331325: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. Your previous answer was [{'action': 'click', 'coordinate': [1144, 108]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1332198: cache has only 0 modules [Step 634] loss_orig = 0.002437, loss_refine = 0.356470 [Step 634] loss_orig = 0.003082, loss_refine = 0.356162 [Step 634] loss_orig = 0.004475, loss_refine = 0.355487 [Step 634] loss_orig = 0.003065, loss_refine = -2.468727 [Step 634] loss_orig = 0.004289, loss_refine = 0.356308 [Step 634] loss_orig = 0.004488, loss_refine = 0.359306 [Step 634] loss_orig = 0.002800, loss_refine = 0.355967 [Step 634] loss_orig = 0.001564, loss_refine = 0.356469 Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1333071: cache has only 0 modules 53%|█████▎ | 635/1208 [8:21:27<7:40:35, 48.23s/it] {'loss': 0.0029, 'grad_norm': 7.055276786354284, 'learning_rate': 4.7433774834437086e-07, 'completion_length': 91.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.06982421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 4.21} 53%|█████▎ | 635/1208 [8:21:27<7:40:35, 48.23s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1333944: cache has only 0 modules Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1334817: cache has only 0 modules 53%|█████▎ | 636/1208 [8:22:09<7:21:10, 46.28s/it] {'loss': 0.0019, 'grad_norm': 37.0636023303283, 'learning_rate': 4.735099337748344e-07, 'completion_length': 98.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.048095703125, 'clip_ratio': 0.0, 'epoch': 4.21} 53%|█████▎ | 636/1208 [8:22:09<7:21:10, 46.28s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1335690: cache has only 0 modules Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1336563: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1337436: cache has only 0 modules [Step 636] loss_orig = 0.001349, loss_refine = 0.936790[Step 636] loss_orig = 0.001699, loss_refine = 0.938800[Step 636] loss_orig = 0.001338, loss_refine = -0.931195 [Step 636] loss_orig = 0.001340, loss_refine = -0.934267 [Step 636] loss_orig = 0.001899, loss_refine = -0.932929[Step 636] loss_orig = 0.001878, loss_refine = 0.939818 [Step 636] loss_orig = 0.001510, loss_refine = 0.937198 [Step 636] loss_orig = 0.000977, loss_refine = -0.932864 53%|█████▎ | 637/1208 [8:22:57<7:26:46, 46.95s/it] {'loss': 0.0026, 'grad_norm': 4.352902113526922, 'learning_rate': 4.72682119205298e-07, 'completion_length': 86.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.0499267578125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 4.22} 53%|█████▎ | 637/1208 [8:22:57<7:26:46, 46.95s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1338309: cache has only 0 modules Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1339182: cache has only 0 modules 53%|█████▎ | 638/1208 [8:23:48<7:36:13, 48.02s/it] {'loss': 0.0027, 'grad_norm': 15.811692475095528, 'learning_rate': 4.7185430463576157e-07, 'completion_length': 123.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.066650390625, 'clip_ratio': 0.0, 'epoch': 4.23} 53%|█████▎ | 638/1208 [8:23:48<7:36:13, 48.02s/it]Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1340055: cache has only 0 modules Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1340928: cache has only 0 modules 53%|█████▎ | 639/1208 [8:24:32<7:25:34, 46.99s/it] {'loss': 0.0019, 'grad_norm': 12.031334344407705, 'learning_rate': 4.7102649006622514e-07, 'completion_length': 100.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.3535533845424652, 'kl': 0.0479736328125, 'clip_ratio': 0.0, 'epoch': 4.23} 53%|█████▎ | 639/1208 [8:24:32<7:25:34, 46.99s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1341801: cache has only 0 modules Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1342674: cache has only 0 modules 53%|█████▎ | 640/1208 [8:25:08<6:52:32, 43.58s/it] {'loss': 0.0022, 'grad_norm': 7.6697242910408665, 'learning_rate': 4.701986754966887e-07, 'completion_length': 92.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.05517578125, 'clip_ratio': 0.0, 'epoch': 4.24} 53%|█████▎ | 640/1208 [8:25:08<6:52:32, 43.58s/it]Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1343547: cache has only 0 modules Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1344420: cache has only 0 modules 53%|█████▎ | 641/1208 [8:25:47<6:37:37, 42.08s/it] {'loss': 0.0017, 'grad_norm': 10.254151026951288, 'learning_rate': 4.693708609271523e-07, 'completion_length': 83.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 4.25} 53%|█████▎ | 641/1208 [8:25:47<6:37:37, 42.08s/it]Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1345293: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1346166: cache has only 0 modules 53%|█████▎ | 642/1208 [8:26:26<6:28:17, 41.16s/it] {'loss': 0.002, 'grad_norm': 12.135274835163047, 'learning_rate': 4.685430463576159e-07, 'completion_length': 98.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0489501953125, 'clip_ratio': 0.0, 'epoch': 4.25} 53%|█████▎ | 642/1208 [8:26:26<6:28:17, 41.16s/it]Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1347039: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1347912: cache has only 0 modules 53%|█████▎ | 643/1208 [8:27:01<6:10:42, 39.37s/it] {'loss': 0.0035, 'grad_norm': 4.988486054027421, 'learning_rate': 4.677152317880794e-07, 'completion_length': 94.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.0870361328125, 'clip_ratio': 0.0, 'epoch': 4.26} 53%|█████▎ | 643/1208 [8:27:01<6:10:42, 39.37s/it]Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1348785: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1349658: cache has only 0 modules 53%|█████▎ | 644/1208 [8:27:40<6:10:10, 39.38s/it] {'loss': 0.0009, 'grad_norm': 0.31146917579510264, 'learning_rate': 4.6688741721854304e-07, 'completion_length': 98.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02301025390625, 'clip_ratio': 0.0, 'epoch': 4.26} 53%|█████▎ | 644/1208 [8:27:40<6:10:10, 39.38s/it]Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1350531: cache has only 0 modules Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1351404: cache has only 0 modules 53%|█████▎ | 645/1208 [8:28:19<6:06:43, 39.08s/it] {'loss': 0.0016, 'grad_norm': 6.750045293970463, 'learning_rate': 4.660596026490066e-07, 'completion_length': 92.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.0390625, 'clip_ratio': 0.0, 'epoch': 4.27} 53%|█████▎ | 645/1208 [8:28:19<6:06:43, 39.08s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1352277: cache has only 0 modules Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1353150: cache has only 0 modules 53%|█████▎ | 646/1208 [8:28:53<5:52:27, 37.63s/it] {'loss': 0.0025, 'grad_norm': 0.4240608626066648, 'learning_rate': 4.652317880794702e-07, 'completion_length': 96.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0626220703125, 'clip_ratio': 0.0, 'epoch': 4.28} 53%|█████▎ | 646/1208 [8:28:53<5:52:27, 37.63s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1354023: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1357, 588]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1354896: cache has only 0 modules [Step 646] loss_orig = 0.001394, loss_refine = 0.542985 [Step 646] loss_orig = 0.002107, loss_refine = 0.542015[Step 646] loss_orig = 0.002596, loss_refine = 0.548776[Step 646] loss_orig = 0.001795, loss_refine = 0.541190 [Step 646] loss_orig = 0.001732, loss_refine = -1.617611 [Step 646] loss_orig = 0.002570, loss_refine = 0.560514 [Step 646] loss_orig = 0.002767, loss_refine = 0.545592 [Step 646] loss_orig = 0.001313, loss_refine = -1.618164 Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1355769: cache has only 0 modules 54%|█████▎ | 647/1208 [8:29:49<6:44:14, 43.23s/it] {'loss': 0.0041, 'grad_norm': 20.54450847170326, 'learning_rate': 4.6440397350993375e-07, 'completion_length': 94.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.0560302734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.28} 54%|█████▎ | 647/1208 [8:29:49<6:44:14, 43.23s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1356642: cache has only 0 modules Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1357515: cache has only 0 modules 54%|█████▎ | 648/1208 [8:30:24<6:20:13, 40.74s/it] {'loss': 0.0036, 'grad_norm': 5.95918125168414, 'learning_rate': 4.635761589403973e-07, 'completion_length': 86.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0902099609375, 'clip_ratio': 0.0, 'epoch': 4.29} 54%|█████▎ | 648/1208 [8:30:24<6:20:13, 40.74s/it]Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1358388: cache has only 0 modules Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1359261: cache has only 0 modules 54%|█████▎ | 649/1208 [8:31:03<6:15:48, 40.34s/it] {'loss': 0.0013, 'grad_norm': 5.348263733253919, 'learning_rate': 4.627483443708609e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.032958984375, 'clip_ratio': 0.0, 'epoch': 4.3} 54%|█████▎ | 649/1208 [8:31:03<6:15:48, 40.34s/it]Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1360134: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1361007: cache has only 0 modules 54%|█████▍ | 650/1208 [8:31:40<6:03:26, 39.08s/it] {'loss': 0.0043, 'grad_norm': 6.438449010064058, 'learning_rate': 4.619205298013245e-07, 'completion_length': 74.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.107421875, 'clip_ratio': 0.0, 'epoch': 4.3} 54%|█████▍ | 650/1208 [8:31:40<6:03:26, 39.08s/it]Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1361880: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. Your previous answer was [{'action': 'click', 'coordinate': [1733, 568]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1362753: cache has only 0 modules [Step 650] loss_orig = 0.001984, loss_refine = 0.002431 [Step 650] loss_orig = 0.000820, loss_refine = 0.001655[Step 650] loss_orig = 0.001073, loss_refine = 0.001648 [Step 650] loss_orig = 0.001486, loss_refine = 0.001370 [Step 650] loss_orig = 0.001219, loss_refine = 0.001923[Step 650] loss_orig = 0.001885, loss_refine = 0.001310 [Step 650] loss_orig = 0.002699, loss_refine = 0.001981 [Step 650] loss_orig = 0.001134, loss_refine = 0.001274 Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1363626: cache has only 0 modules 54%|█████▍ | 651/1208 [8:32:39<6:58:29, 45.08s/it] {'loss': 0.0021, 'grad_norm': 7.040791460331101, 'learning_rate': 4.6109271523178803e-07, 'completion_length': 98.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.1666666666666665, 'reward_std': 0.17817415793736777, 'kl': 0.05029296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 4.31} 54%|█████▍ | 651/1208 [8:32:39<6:58:29, 45.08s/it]Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1364499: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1365372: cache has only 0 modules 54%|█████▍ | 652/1208 [8:33:13<6:27:43, 41.84s/it] {'loss': 0.002, 'grad_norm': 0.5105361908184546, 'learning_rate': 4.6026490066225166e-07, 'completion_length': 78.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0491943359375, 'clip_ratio': 0.0, 'epoch': 4.32} 54%|█████▍ | 652/1208 [8:33:13<6:27:43, 41.84s/it]Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1366245: cache has only 0 modules Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1367118: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1367991: cache has only 0 modules [Step 652] loss_orig = 0.001002, loss_refine = 0.937342[Step 652] loss_orig = 0.001083, loss_refine = 0.937258[Step 652] loss_orig = 0.001620, loss_refine = 0.936764 [Step 652] loss_orig = 0.001196, loss_refine = -0.934014 [Step 652] loss_orig = 0.001382, loss_refine = 0.937013 [Step 652] loss_orig = 0.000357, loss_refine = -0.934477 [Step 652] loss_orig = 0.000701, loss_refine = -0.934798 [Step 652] loss_orig = 0.000878, loss_refine = -0.934109 54%|█████▍ | 653/1208 [8:34:11<7:12:43, 46.78s/it] {'loss': 0.0019, 'grad_norm': 10.244823425372351, 'learning_rate': 4.594370860927152e-07, 'completion_length': 101.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.2960252861181895, 'kl': 0.0433349609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 4.32} 54%|█████▍ | 653/1208 [8:34:11<7:12:43, 46.78s/it]Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1368864: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1369737: cache has only 0 modules [Step 653] loss_orig = 0.000731, loss_refine = 0.354847[Step 653] loss_orig = 0.001028, loss_refine = -2.466952[Step 653] loss_orig = 0.001710, loss_refine = 0.355059 [Step 653] loss_orig = 0.001233, loss_refine = 0.355113 [Step 653] loss_orig = 0.001245, loss_refine = 0.354221[Step 653] loss_orig = 0.002317, loss_refine = 0.356024 [Step 653] loss_orig = 0.000978, loss_refine = 0.354710 [Step 653] loss_orig = 0.001603, loss_refine = 0.355425 Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1370610: cache has only 0 modules 54%|█████▍ | 654/1208 [8:35:00<7:16:07, 47.23s/it] {'loss': 0.0016, 'grad_norm': 29.55374218284575, 'learning_rate': 4.586092715231788e-07, 'completion_length': 87.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.0291748046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 4.33} 54%|█████▍ | 654/1208 [8:35:00<7:16:07, 47.23s/it]Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1371483: cache has only 0 modules Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1372356: cache has only 0 modules 54%|█████▍ | 655/1208 [8:35:49<7:22:35, 48.02s/it] {'loss': 0.0035, 'grad_norm': 5.003668823220617, 'learning_rate': 4.5778145695364237e-07, 'completion_length': 97.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.08837890625, 'clip_ratio': 0.0, 'epoch': 4.34} 54%|█████▍ | 655/1208 [8:35:49<7:22:35, 48.02s/it]Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1373229: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1374102: cache has only 0 modules 54%|█████▍ | 656/1208 [8:36:35<7:14:37, 47.24s/it] {'loss': 0.0015, 'grad_norm': 3.639493731005843, 'learning_rate': 4.5695364238410594e-07, 'completion_length': 109.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.03857421875, 'clip_ratio': 0.0, 'epoch': 4.34} 54%|█████▍ | 656/1208 [8:36:35<7:14:37, 47.24s/it]Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1374975: cache has only 0 modules Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1375848: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1376721: cache has only 0 modules [Step 656] loss_orig = 0.000951, loss_refine = -1.981173[Step 656] loss_orig = 0.001160, loss_refine = -0.659565 [Step 656] loss_orig = 0.002466, loss_refine = 0.663473 [Step 656] loss_orig = 0.002413, loss_refine = 0.663057 [Step 656] loss_orig = 0.000764, loss_refine = -0.659889 [Step 656] loss_orig = 0.000886, loss_refine = 0.662645 [Step 656] loss_orig = 0.001198, loss_refine = 0.666545 [Step 656] loss_orig = 0.001420, loss_refine = 0.662333 54%|█████▍ | 657/1208 [8:37:29<7:34:20, 49.48s/it] {'loss': 0.003, 'grad_norm': 10.426629978129526, 'learning_rate': 4.5612582781456956e-07, 'completion_length': 96.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.4244926969210307, 'kl': 0.065673828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 4.35} 54%|█████▍ | 657/1208 [8:37:29<7:34:20, 49.48s/it]Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1377594: cache has only 0 modules Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1378467: cache has only 0 modules 54%|█████▍ | 658/1208 [8:38:07<6:59:47, 45.80s/it] {'loss': 0.002, 'grad_norm': 13.080561811596656, 'learning_rate': 4.552980132450331e-07, 'completion_length': 87.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4355512708425522, 'kl': 0.0504150390625, 'clip_ratio': 0.0, 'epoch': 4.36} 54%|█████▍ | 658/1208 [8:38:07<6:59:47, 45.80s/it]Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1379340: cache has only 0 modules Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1380213: cache has only 0 modules 55%|█████▍ | 659/1208 [8:38:45<6:37:25, 43.43s/it] {'loss': 0.0026, 'grad_norm': 6.262490708436836, 'learning_rate': 4.544701986754967e-07, 'completion_length': 94.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0640869140625, 'clip_ratio': 0.0, 'epoch': 4.36} 55%|█████▍ | 659/1208 [8:38:45<6:37:25, 43.43s/it]Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1381086: cache has only 0 modules Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1381959: cache has only 0 modules 55%|█████▍ | 660/1208 [8:39:22<6:20:14, 41.63s/it] {'loss': 0.0016, 'grad_norm': 11.478837732844323, 'learning_rate': 4.536423841059602e-07, 'completion_length': 92.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.04052734375, 'clip_ratio': 0.0, 'epoch': 4.37} 55%|█████▍ | 660/1208 [8:39:22<6:20:14, 41.63s/it]Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1382832: cache has only 0 modules Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1383705: cache has only 0 modules 55%|█████▍ | 661/1208 [8:39:59<6:05:29, 40.09s/it] {'loss': 0.0013, 'grad_norm': 0.36256063383462545, 'learning_rate': 4.5281456953642384e-07, 'completion_length': 84.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03265380859375, 'clip_ratio': 0.0, 'epoch': 4.38} 55%|█████▍ | 661/1208 [8:39:59<6:05:29, 40.09s/it]Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1384578: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1385451: cache has only 0 modules [Step 661] loss_orig = 0.002230, loss_refine = -0.350663 [Step 661] loss_orig = 0.002093, loss_refine = -0.351045[Step 661] loss_orig = 0.001150, loss_refine = -0.350165 [Step 661] loss_orig = 0.001182, loss_refine = 2.476421 [Step 661] loss_orig = 0.001411, loss_refine = -0.351014[Step 661] loss_orig = 0.002321, loss_refine = -0.346689 [Step 661] loss_orig = 0.001474, loss_refine = -0.351809 [Step 661] loss_orig = 0.001649, loss_refine = -0.352078 Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1386324: cache has only 0 modules 55%|█████▍ | 662/1208 [8:40:50<6:34:31, 43.35s/it] {'loss': 0.002, 'grad_norm': 36.68422138006409, 'learning_rate': 4.5198675496688736e-07, 'completion_length': 86.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.03558349609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 4.38} 55%|█████▍ | 662/1208 [8:40:50<6:34:31, 43.35s/it]Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1387197: cache has only 0 modules Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1388070: cache has only 0 modules 55%|█████▍ | 663/1208 [8:41:36<6:43:32, 44.43s/it] {'loss': 0.0025, 'grad_norm': 8.44727831028076, 'learning_rate': 4.51158940397351e-07, 'completion_length': 107.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4355512708425522, 'kl': 0.0625, 'clip_ratio': 0.0, 'epoch': 4.39} 55%|█████▍ | 663/1208 [8:41:36<6:43:32, 44.43s/it]Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1388943: cache has only 0 modules Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1389816: cache has only 0 modules 55%|█████▍ | 664/1208 [8:42:19<6:38:34, 43.96s/it] {'loss': 0.0018, 'grad_norm': 5.654913244519992, 'learning_rate': 4.5033112582781455e-07, 'completion_length': 105.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.04425048828125, 'clip_ratio': 0.0, 'epoch': 4.4} 55%|█████▍ | 664/1208 [8:42:19<6:38:34, 43.96s/it]Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1390689: cache has only 0 modules Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1391562: cache has only 0 modules 55%|█████▌ | 665/1208 [8:42:55<6:14:56, 41.43s/it] {'loss': 0.0024, 'grad_norm': 15.598206331867566, 'learning_rate': 4.495033112582781e-07, 'completion_length': 95.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.5303300768136978, 'kl': 0.0596923828125, 'clip_ratio': 0.0, 'epoch': 4.4} 55%|█████▌ | 665/1208 [8:42:55<6:14:56, 41.43s/it]Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1392435: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1393308: cache has only 0 modules [Step 665] loss_orig = 0.001010, loss_refine = -0.538253[Step 665] loss_orig = 0.000811, loss_refine = 1.621802[Step 665] loss_orig = 0.000826, loss_refine = -0.537677 [Step 665] loss_orig = 0.000606, loss_refine = 1.678177 [Step 665] loss_orig = 0.000890, loss_refine = -0.537847 [Step 665] loss_orig = 0.001423, loss_refine = -0.536281 [Step 665] loss_orig = 0.000945, loss_refine = -0.536703[Step 665] loss_orig = 0.001045, loss_refine = -0.537392 Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1394181: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more settings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1395054: cache has only 0 modules [Step 665] loss_orig = 0.002625, loss_refine = -0.933289 [Step 665] loss_orig = 0.006580, loss_refine = 0.939700 [Step 665] loss_orig = 0.001647, loss_refine = -0.933142 [Step 665] loss_orig = 0.006983, loss_refine = 0.939829 [Step 665] loss_orig = 0.028973, loss_refine = -0.933132[Step 665] loss_orig = 0.004199, loss_refine = -0.931968 [Step 665] loss_orig = 0.005235, loss_refine = 0.937907 [Step 665] loss_orig = 0.003317, loss_refine = 0.937780 55%|█████▌ | 666/1208 [8:44:06<7:33:42, 50.23s/it] {'loss': 0.0062, 'grad_norm': 8.063241796322183, 'learning_rate': 4.486754966887417e-07, 'completion_length': 91.84375, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.3650856465101242, 'kl': 0.1046142578125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.9375, 'epoch': 4.41} 55%|█████▌ | 666/1208 [8:44:06<7:33:42, 50.23s/it]Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1395927: cache has only 0 modules Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1396800: cache has only 0 modules 55%|█████▌ | 667/1208 [8:44:48<7:11:38, 47.87s/it] {'loss': 0.0021, 'grad_norm': 7.734720540978366, 'learning_rate': 4.4784768211920526e-07, 'completion_length': 115.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.3204349875450134, 'kl': 0.052978515625, 'clip_ratio': 0.0, 'epoch': 4.42} 55%|█████▌ | 667/1208 [8:44:48<7:11:38, 47.87s/it]Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1397673: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1398546: cache has only 0 modules [Step 667] loss_orig = 2.477495, loss_refine = -1.618124 [Step 667] loss_orig = -0.352540, loss_refine = -1.618725[Step 667] loss_orig = -0.352154, loss_refine = 0.541784[Step 667] loss_orig = -0.352160, loss_refine = 0.541111 [Step 667] loss_orig = -0.352089, loss_refine = 0.540825 [Step 667] loss_orig = -0.352263, loss_refine = 0.542801[Step 667] loss_orig = -0.351977, loss_refine = 0.541396 [Step 667] loss_orig = -0.352508, loss_refine = 0.545027 Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1399419: cache has only 0 modules 55%|█████▌ | 668/1208 [8:45:44<7:32:58, 50.33s/it] {'loss': 0.0017, 'grad_norm': 4.704615202190618, 'learning_rate': 4.470198675496689e-07, 'completion_length': 88.45833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.0367431640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.42} 55%|█████▌ | 668/1208 [8:45:44<7:32:58, 50.33s/it]Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1400292: cache has only 0 modules Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1401165: cache has only 0 modules 55%|█████▌ | 669/1208 [8:46:22<7:00:03, 46.76s/it] {'loss': 0.002, 'grad_norm': 0.31109680580591614, 'learning_rate': 4.461920529801324e-07, 'completion_length': 87.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04931640625, 'clip_ratio': 0.0, 'epoch': 4.43} 55%|█████▌ | 669/1208 [8:46:22<7:00:03, 46.76s/it]Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1402038: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1402911: cache has only 0 modules 55%|█████▌ | 670/1208 [8:46:57<6:25:30, 42.99s/it] {'loss': 0.002, 'grad_norm': 3.721251573919157, 'learning_rate': 4.45364238410596e-07, 'completion_length': 101.4375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.051025390625, 'clip_ratio': 0.0, 'epoch': 4.44} 55%|█████▌ | 670/1208 [8:46:57<6:25:30, 42.99s/it]Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1403784: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1404657: cache has only 0 modules [Step 670] loss_orig = 0.001067, loss_refine = 0.001638[Step 670] loss_orig = 0.001932, loss_refine = 0.002054[Step 670] loss_orig = 0.002322, loss_refine = 0.001664[Step 670] loss_orig = 0.002238, loss_refine = 0.001795 [Step 670] loss_orig = 0.001714, loss_refine = 0.004750[Step 670] loss_orig = 0.004878, loss_refine = 0.004004[Step 670] loss_orig = 0.001354, loss_refine = 0.001383 [Step 670] loss_orig = 0.002128, loss_refine = 0.001120 Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1405530: cache has only 0 modules 56%|█████▌ | 671/1208 [8:47:55<7:04:51, 47.47s/it] {'loss': 0.0018, 'grad_norm': 5.40971364198872, 'learning_rate': 4.445364238410596e-07, 'completion_length': 101.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.044677734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 4.44} 56%|█████▌ | 671/1208 [8:47:55<7:04:51, 47.47s/it]Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1406403: cache has only 0 modules Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1407276: cache has only 0 modules 56%|█████▌ | 672/1208 [8:48:38<6:54:23, 46.39s/it] {'loss': 0.0017, 'grad_norm': 0.3382989314281791, 'learning_rate': 4.4370860927152317e-07, 'completion_length': 100.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'epoch': 4.45} 56%|█████▌ | 672/1208 [8:48:38<6:54:23, 46.39s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1408149: cache has only 0 modules Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1409022: cache has only 0 modules 56%|█████▌ | 673/1208 [8:49:20<6:41:04, 44.98s/it] {'loss': 0.0017, 'grad_norm': 8.06579249068811, 'learning_rate': 4.4288079470198674e-07, 'completion_length': 102.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.043212890625, 'clip_ratio': 0.0, 'epoch': 4.46} 56%|█████▌ | 673/1208 [8:49:20<6:41:04, 44.98s/it]Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1409895: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1410768: cache has only 0 modules 56%|█████▌ | 674/1208 [8:49:56<6:15:48, 42.23s/it] {'loss': 0.0024, 'grad_norm': 3.9499591541081247, 'learning_rate': 4.420529801324503e-07, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0587158203125, 'clip_ratio': 0.0, 'epoch': 4.46} 56%|█████▌ | 674/1208 [8:49:56<6:15:48, 42.23s/it]Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1411641: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1412514: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [454, 445]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1413387: cache has only 0 modules [Step 674] loss_orig = 0.002096, loss_refine = 0.004832[Step 674] loss_orig = 0.003211, loss_refine = 0.001825 [Step 674] loss_orig = 0.001487, loss_refine = 0.006851 [Step 674] loss_orig = 0.002936, loss_refine = 0.002825 [Step 674] loss_orig = 0.001399, loss_refine = 0.002748 [Step 674] loss_orig = 0.001178, loss_refine = 0.003781 [Step 674] loss_orig = 0.003147, loss_refine = 0.009024 [Step 674] loss_orig = 0.001395, loss_refine = 0.002275 56%|█████▌ | 675/1208 [8:51:02<7:18:01, 49.31s/it] {'loss': 0.0028, 'grad_norm': 4.090657460961397, 'learning_rate': 4.412251655629139e-07, 'completion_length': 106.16666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.875, 'reward_std': 0.35355337460835773, 'kl': 0.04296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 4.47} 56%|█████▌ | 675/1208 [8:51:02<7:18:01, 49.31s/it]Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1414260: cache has only 0 modules Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1415133: cache has only 0 modules 56%|█████▌ | 676/1208 [8:51:41<6:50:21, 46.28s/it] {'loss': 0.001, 'grad_norm': 0.29942123464626635, 'learning_rate': 4.4039735099337745e-07, 'completion_length': 94.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0242919921875, 'clip_ratio': 0.0, 'epoch': 4.48} 56%|█████▌ | 676/1208 [8:51:41<6:50:21, 46.28s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1416006: cache has only 0 modules Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1416879: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [998, 957]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1417752: cache has only 0 modules [Step 676] loss_orig = 0.000625, loss_refine = 0.359337 [Step 676] loss_orig = 0.002926, loss_refine = 0.354345 [Step 676] loss_orig = 0.001973, loss_refine = 0.354563[Step 676] loss_orig = 0.006274, loss_refine = -2.472768 [Step 676] loss_orig = 0.000918, loss_refine = 0.354602 [Step 676] loss_orig = 0.001410, loss_refine = 0.360233 [Step 676] loss_orig = 0.002004, loss_refine = 0.354136 [Step 676] loss_orig = 0.001378, loss_refine = 0.354734 56%|█████▌ | 677/1208 [8:52:35<7:11:12, 48.72s/it] {'loss': 0.0022, 'grad_norm': 16.297408271905706, 'learning_rate': 4.39569536423841e-07, 'completion_length': 84.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.0523681640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 4.48} 56%|█████▌ | 677/1208 [8:52:35<7:11:12, 48.72s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1418625: cache has only 0 modules Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1419498: cache has only 0 modules 56%|█████▌ | 678/1208 [8:53:14<6:42:47, 45.60s/it] {'loss': 0.0014, 'grad_norm': 4.45632061569112, 'learning_rate': 4.3874172185430464e-07, 'completion_length': 94.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.034423828125, 'clip_ratio': 0.0, 'epoch': 4.49} 56%|█████▌ | 678/1208 [8:53:14<6:42:47, 45.60s/it]Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1420371: cache has only 0 modules Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1421244: cache has only 0 modules 56%|█████▌ | 679/1208 [8:54:02<6:48:37, 46.35s/it] {'loss': 0.0019, 'grad_norm': 6.461940580707681, 'learning_rate': 4.3791390728476816e-07, 'completion_length': 112.875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.7071067541837692, 'kl': 0.047607421875, 'clip_ratio': 0.0, 'epoch': 4.5} 56%|█████▌ | 679/1208 [8:54:02<6:48:37, 46.35s/it]Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1422117: cache has only 0 modules Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1422990: cache has only 0 modules 56%|█████▋ | 680/1208 [8:54:44<6:36:41, 45.08s/it] {'loss': 0.0022, 'grad_norm': 7.073553883697109, 'learning_rate': 4.370860927152318e-07, 'completion_length': 96.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.055419921875, 'clip_ratio': 0.0, 'epoch': 4.5} 56%|█████▋ | 680/1208 [8:54:44<6:36:41, 45.08s/it]Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1423863: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1424736: cache has only 0 modules 56%|█████▋ | 681/1208 [8:55:19<6:08:56, 42.01s/it] {'loss': 0.0025, 'grad_norm': 4.893214579732293, 'learning_rate': 4.3625827814569535e-07, 'completion_length': 86.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0634765625, 'clip_ratio': 0.0, 'epoch': 4.51} 56%|█████▋ | 681/1208 [8:55:19<6:08:56, 42.01s/it]Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1425609: cache has only 0 modules Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1426482: cache has only 0 modules 56%|█████▋ | 682/1208 [8:55:58<6:01:40, 41.26s/it] {'loss': 0.0016, 'grad_norm': 12.705303222387622, 'learning_rate': 4.354304635761589e-07, 'completion_length': 87.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.038818359375, 'clip_ratio': 0.0, 'epoch': 4.52} 56%|█████▋ | 682/1208 [8:55:58<6:01:40, 41.26s/it]Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1427355: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1428228: cache has only 0 modules 57%|█████▋ | 683/1208 [8:56:39<5:58:50, 41.01s/it] {'loss': 0.0014, 'grad_norm': 29.926273434092447, 'learning_rate': 4.3460264900662254e-07, 'completion_length': 101.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.7891046404838562, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'epoch': 4.52} 57%|█████▋ | 683/1208 [8:56:39<5:58:50, 41.01s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1429101: cache has only 0 modules Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1429974: cache has only 0 modules 57%|█████▋ | 684/1208 [8:57:12<5:37:59, 38.70s/it] {'loss': 0.001, 'grad_norm': 4.616566324407469, 'learning_rate': 4.3377483443708606e-07, 'completion_length': 89.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 0.9375, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02490234375, 'clip_ratio': 0.0, 'epoch': 4.53} 57%|█████▋ | 684/1208 [8:57:12<5:37:59, 38.70s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1430847: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1431720: cache has only 0 modules [Step 684] loss_orig = 0.003921, loss_refine = 0.542317[Step 684] loss_orig = 0.003205, loss_refine = 0.541239 [Step 684] loss_orig = 0.001732, loss_refine = 0.541097[Step 684] loss_orig = 0.004386, loss_refine = -1.616789 [Step 684] loss_orig = 0.001316, loss_refine = -1.618319 [Step 684] loss_orig = 0.001773, loss_refine = 0.542529 [Step 684] loss_orig = 0.001717, loss_refine = 0.543702[Step 684] loss_orig = 0.001742, loss_refine = 0.541665 Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1432593: cache has only 0 modules 57%|█████▋ | 685/1208 [8:58:00<6:00:24, 41.35s/it] {'loss': 0.0018, 'grad_norm': 6.354588923778202, 'learning_rate': 4.329470198675497e-07, 'completion_length': 99.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.0494384765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.54} 57%|█████▋ | 685/1208 [8:58:00<6:00:24, 41.35s/it]Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1433466: cache has only 0 modules Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1434339: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'choose watercolor brush style'. Your previous answer was [{'action': 'click', 'coordinate': [570, 2266]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward errorcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1435212: cache has only 0 modules [Step 685] loss_orig = 0.001263, loss_refine = -1.869070[Step 685] loss_orig = 0.001141, loss_refine = 0.002299 [Step 685] loss_orig = 0.000779, loss_refine = 0.000939 [Step 685] loss_orig = 0.001015, loss_refine = 0.001459 [Step 685] loss_orig = 0.001209, loss_refine = 1.873518 [Step 685] loss_orig = 0.001189, loss_refine = 0.002386[Step 685] loss_orig = 0.002243, loss_refine = 0.001510 [Step 685] loss_orig = 0.001620, loss_refine = 0.008768 57%|█████▋ | 686/1208 [8:58:54<6:34:35, 45.36s/it] {'loss': 0.0021, 'grad_norm': 5.899031021243793, 'learning_rate': 4.321192052980132e-07, 'completion_length': 96.70833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.33247750997543335, 'kl': 0.0340576171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 4.54} 57%|█████▋ | 686/1208 [8:58:54<6:34:35, 45.36s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1436085: cache has only 0 modules Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1436958: cache has only 0 modules 57%|█████▋ | 687/1208 [8:59:33<6:15:51, 43.28s/it] {'loss': 0.0013, 'grad_norm': 4.446883787591196, 'learning_rate': 4.312913907284768e-07, 'completion_length': 100.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0325927734375, 'clip_ratio': 0.0, 'epoch': 4.55} 57%|█████▋ | 687/1208 [8:59:33<6:15:51, 43.28s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1437831: cache has only 0 modules Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1438704: cache has only 0 modules 57%|█████▋ | 688/1208 [9:00:17<6:16:58, 43.50s/it] {'loss': 0.0019, 'grad_norm': 10.679285269900829, 'learning_rate': 4.3046357615894034e-07, 'completion_length': 100.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0484619140625, 'clip_ratio': 0.0, 'epoch': 4.56} 57%|█████▋ | 688/1208 [9:00:17<6:16:58, 43.50s/it]Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1439577: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1440450: cache has only 0 modules 57%|█████▋ | 689/1208 [9:00:58<6:11:50, 42.99s/it] {'loss': 0.0013, 'grad_norm': 4.77711797904149, 'learning_rate': 4.2963576158940396e-07, 'completion_length': 98.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03265380859375, 'clip_ratio': 0.0, 'epoch': 4.56} 57%|█████▋ | 689/1208 [9:00:58<6:11:50, 42.99s/it]Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1441323: cache has only 0 modules Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1442196: cache has only 0 modules 57%|█████▋ | 690/1208 [9:01:35<5:55:21, 41.16s/it] {'loss': 0.0017, 'grad_norm': 6.40359813547391, 'learning_rate': 4.2880794701986753e-07, 'completion_length': 91.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0430908203125, 'clip_ratio': 0.0, 'epoch': 4.57} 57%|█████▋ | 690/1208 [9:01:35<5:55:21, 41.16s/it]Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1443069: cache has only 0 modules Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1443942: cache has only 0 modules 57%|█████▋ | 691/1208 [9:02:19<6:00:42, 41.86s/it] {'loss': 0.0014, 'grad_norm': 9.698438432710953, 'learning_rate': 4.279801324503311e-07, 'completion_length': 116.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.49022960662841797, 'kl': 0.034912109375, 'clip_ratio': 0.0, 'epoch': 4.58} 57%|█████▋ | 691/1208 [9:02:19<6:00:42, 41.86s/it]Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1444815: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1445688: cache has only 0 modules [Step 691] loss_orig = 0.001320, loss_refine = 0.003190[Step 691] loss_orig = 0.001709, loss_refine = 0.002389 [Step 691] loss_orig = 0.003783, loss_refine = 0.001935[Step 691] loss_orig = 0.004376, loss_refine = 0.002003 [Step 691] loss_orig = 0.001170, loss_refine = 0.002090 [Step 691] loss_orig = 0.002545, loss_refine = 0.002184 [Step 691] loss_orig = 0.001600, loss_refine = 0.006467 [Step 691] loss_orig = 0.001691, loss_refine = 0.002336 Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1446561: cache has only 0 modules 57%|█████▋ | 692/1208 [9:03:11<6:25:13, 44.79s/it] {'loss': 0.0019, 'grad_norm': 0.4369569499349374, 'learning_rate': 4.271523178807947e-07, 'completion_length': 97.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 4.58} 57%|█████▋ | 692/1208 [9:03:11<6:25:13, 44.79s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1447434: cache has only 0 modules Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1448307: cache has only 0 modules 57%|█████▋ | 693/1208 [9:03:43<5:53:13, 41.15s/it] {'loss': 0.0029, 'grad_norm': 0.6440124047158912, 'learning_rate': 4.2632450331125824e-07, 'completion_length': 86.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.073486328125, 'clip_ratio': 0.0, 'epoch': 4.59} 57%|█████▋ | 693/1208 [9:03:43<5:53:13, 41.15s/it]Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1449180: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1450053: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Channel watermark'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1450926: cache has only 0 modules [Step 693] loss_orig = 0.001091, loss_refine = 0.355082[Step 693] loss_orig = 0.005099, loss_refine = 0.354899[Step 693] loss_orig = 0.001186, loss_refine = 0.355435[Step 693] loss_orig = 0.003226, loss_refine = 0.355202[Step 693] loss_orig = 0.002053, loss_refine = 0.354431 [Step 693] loss_orig = 0.002344, loss_refine = 0.354512 [Step 693] loss_orig = 0.000963, loss_refine = 0.354399 [Step 693] loss_orig = 0.001659, loss_refine = -2.470290 57%|█████▋ | 694/1208 [9:04:37<6:24:06, 44.84s/it] {'loss': 0.0022, 'grad_norm': 8.437183247306066, 'learning_rate': 4.2549668874172187e-07, 'completion_length': 97.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.27215448021888733, 'kl': 0.06005859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 4.6} 57%|█████▋ | 694/1208 [9:04:37<6:24:06, 44.84s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1451799: cache has only 0 modules Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1452672: cache has only 0 modules 58%|█████▊ | 695/1208 [9:05:10<5:53:44, 41.37s/it] {'loss': 0.0013, 'grad_norm': 4.261793667386178, 'learning_rate': 4.246688741721854e-07, 'completion_length': 90.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0321044921875, 'clip_ratio': 0.0, 'epoch': 4.6} 58%|█████▊ | 695/1208 [9:05:10<5:53:44, 41.37s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1453545: cache has only 0 modules Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1454418: cache has only 0 modules 58%|█████▊ | 696/1208 [9:05:51<5:53:15, 41.40s/it] {'loss': 0.0018, 'grad_norm': 6.239851378188172, 'learning_rate': 4.23841059602649e-07, 'completion_length': 92.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0452880859375, 'clip_ratio': 0.0, 'epoch': 4.61} 58%|█████▊ | 696/1208 [9:05:51<5:53:15, 41.40s/it]Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1455291: cache has only 0 modules Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1456164: cache has only 0 modules 58%|█████▊ | 697/1208 [9:06:33<5:52:53, 41.44s/it] {'loss': 0.0015, 'grad_norm': 6.430520187055669, 'learning_rate': 4.230132450331126e-07, 'completion_length': 104.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.037353515625, 'clip_ratio': 0.0, 'epoch': 4.62} 58%|█████▊ | 697/1208 [9:06:33<5:52:53, 41.44s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1457037: cache has only 0 modules Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1457910: cache has only 0 modules 58%|█████▊ | 698/1208 [9:07:14<5:50:44, 41.26s/it] {'loss': 0.0016, 'grad_norm': 3.3879344803115328, 'learning_rate': 4.2218543046357615e-07, 'completion_length': 102.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.039306640625, 'clip_ratio': 0.0, 'epoch': 4.62} 58%|█████▊ | 698/1208 [9:07:14<5:50:44, 41.26s/it]Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1458783: cache has only 0 modules Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1459656: cache has only 0 modules 58%|█████▊ | 699/1208 [9:07:53<5:46:02, 40.79s/it] {'loss': 0.0014, 'grad_norm': 3.5220439563016925, 'learning_rate': 4.213576158940397e-07, 'completion_length': 100.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.03466796875, 'clip_ratio': 0.0, 'epoch': 4.63} 58%|█████▊ | 699/1208 [9:07:53<5:46:02, 40.79s/it]Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1460529: cache has only 0 modules Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1461402: cache has only 0 modules 58%|█████▊ | 700/1208 [9:08:30<5:34:17, 39.48s/it] {'loss': 0.0019, 'grad_norm': 10.91408374480007, 'learning_rate': 4.205298013245033e-07, 'completion_length': 96.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0479736328125, 'clip_ratio': 0.0, 'epoch': 4.64} 58%|█████▊ | 700/1208 [9:08:30<5:34:17, 39.48s/it]Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1462275: cache has only 0 modules Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1463148: cache has only 0 modules 58%|█████▊ | 701/1208 [9:09:09<5:33:56, 39.52s/it] {'loss': 0.0018, 'grad_norm': 12.051900371515023, 'learning_rate': 4.1970198675496686e-07, 'completion_length': 104.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.04412841796875, 'clip_ratio': 0.0, 'epoch': 4.64} 58%|█████▊ | 701/1208 [9:09:09<5:33:56, 39.52s/it]Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1464021: cache has only 0 modules Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1464894: cache has only 0 modules 58%|█████▊ | 702/1208 [9:09:59<5:57:54, 42.44s/it] {'loss': 0.0025, 'grad_norm': 6.28745068645629, 'learning_rate': 4.188741721854304e-07, 'completion_length': 109.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.0614013671875, 'clip_ratio': 0.0, 'epoch': 4.65} 58%|█████▊ | 702/1208 [9:09:59<5:57:54, 42.44s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1465767: cache has only 0 modules Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1466640: cache has only 0 modules 58%|█████▊ | 703/1208 [9:10:40<5:55:01, 42.18s/it] {'loss': 0.0018, 'grad_norm': 36.370565487293085, 'learning_rate': 4.18046357615894e-07, 'completion_length': 103.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0460205078125, 'clip_ratio': 0.0, 'epoch': 4.66} 58%|█████▊ | 703/1208 [9:10:40<5:55:01, 42.18s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1467513: cache has only 0 modules Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1468386: cache has only 0 modules 58%|█████▊ | 704/1208 [9:11:21<5:51:19, 41.82s/it] {'loss': 0.0022, 'grad_norm': 39.68371983006314, 'learning_rate': 4.172185430463576e-07, 'completion_length': 99.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0556640625, 'clip_ratio': 0.0, 'epoch': 4.66} 58%|█████▊ | 704/1208 [9:11:21<5:51:19, 41.82s/it]Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1469259: cache has only 0 modules Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1470132: cache has only 0 modules 58%|█████▊ | 705/1208 [9:12:04<5:53:14, 42.14s/it] {'loss': 0.0017, 'grad_norm': 3.96552658263796, 'learning_rate': 4.163907284768212e-07, 'completion_length': 94.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0421142578125, 'clip_ratio': 0.0, 'epoch': 4.67} 58%|█████▊ | 705/1208 [9:12:04<5:53:14, 42.14s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1471005: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1471878: cache has only 0 modules 58%|█████▊ | 706/1208 [9:12:47<5:53:09, 42.21s/it] {'loss': 0.0018, 'grad_norm': 6.570399622969849, 'learning_rate': 4.1556291390728476e-07, 'completion_length': 96.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.04400634765625, 'clip_ratio': 0.0, 'epoch': 4.68} 58%|█████▊ | 706/1208 [9:12:47<5:53:09, 42.21s/it]Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1472751: cache has only 0 modules Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1473624: cache has only 0 modules 59%|█████▊ | 707/1208 [9:13:23<5:37:45, 40.45s/it] {'loss': 0.0014, 'grad_norm': 4.485110069891806, 'learning_rate': 4.1473509933774833e-07, 'completion_length': 93.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.033935546875, 'clip_ratio': 0.0, 'epoch': 4.68} 59%|█████▊ | 707/1208 [9:13:23<5:37:45, 40.45s/it]Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1474497: cache has only 0 modules Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1475370: cache has only 0 modules 59%|█████▊ | 708/1208 [9:13:58<5:23:33, 38.83s/it] {'loss': 0.0009, 'grad_norm': 0.17067143381452965, 'learning_rate': 4.139072847682119e-07, 'completion_length': 86.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02288818359375, 'clip_ratio': 0.0, 'epoch': 4.69} 59%|█████▊ | 708/1208 [9:13:58<5:23:33, 38.83s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1476243: cache has only 0 modules Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1477116: cache has only 0 modules 59%|█████▊ | 709/1208 [9:14:36<5:20:16, 38.51s/it] {'loss': 0.0016, 'grad_norm': 0.44395536027875615, 'learning_rate': 4.1307947019867547e-07, 'completion_length': 86.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0389404296875, 'clip_ratio': 0.0, 'epoch': 4.7} 59%|█████▊ | 709/1208 [9:14:36<5:20:16, 38.51s/it]Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1477989: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1478862: cache has only 0 modules 59%|█████▉ | 710/1208 [9:15:11<5:11:27, 37.53s/it] {'loss': 0.0043, 'grad_norm': 4.052907329015281, 'learning_rate': 4.1225165562913904e-07, 'completion_length': 89.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.1065673828125, 'clip_ratio': 0.0, 'epoch': 4.7} 59%|█████▉ | 710/1208 [9:15:11<5:11:27, 37.53s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1479735: cache has only 0 modules Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1480608: cache has only 0 modules 59%|█████▉ | 711/1208 [9:15:58<5:35:44, 40.53s/it] {'loss': 0.0021, 'grad_norm': 6.022451746830123, 'learning_rate': 4.1142384105960266e-07, 'completion_length': 97.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.7071067541837692, 'kl': 0.0533447265625, 'clip_ratio': 0.0, 'epoch': 4.71} 59%|█████▉ | 711/1208 [9:15:58<5:35:44, 40.53s/it]Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1481481: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1482354: cache has only 0 modules [Step 711] loss_orig = 0.001843, loss_refine = 0.002378[Step 711] loss_orig = 0.001388, loss_refine = 0.002839[Step 711] loss_orig = 0.001413, loss_refine = 0.005185[Step 711] loss_orig = 0.003471, loss_refine = 0.001926 [Step 711] loss_orig = 0.003674, loss_refine = 0.001610 [Step 711] loss_orig = 0.001484, loss_refine = 0.003255 [Step 711] loss_orig = 0.013817, loss_refine = 0.001970 [Step 711] loss_orig = 0.002558, loss_refine = 0.001928 Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1483227: cache has only 0 modules 59%|█████▉ | 712/1208 [9:17:01<6:29:23, 47.10s/it] {'loss': 0.0034, 'grad_norm': 0.8012525212246592, 'learning_rate': 4.105960264900662e-07, 'completion_length': 99.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.099365234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 4.72} 59%|█████▉ | 712/1208 [9:17:01<6:29:23, 47.10s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1484100: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.625 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1484973: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. Your previous answer was [{'action': 'click', 'coordinate': [1883, 215]}, {'action': 'click', 'coordinate': [1883, 215]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1485846: cache has only 0 modules [Step 712] loss_orig = -0.680703, loss_refine = 0.662026[Step 712] loss_orig = -0.673273, loss_refine = -1.981124 [Step 712] loss_orig = 0.413009, loss_refine = -0.660140 [Step 712] loss_orig = -0.678495, loss_refine = 0.663521 [Step 712] loss_orig = -0.680513, loss_refine = 0.662363 [Step 712] loss_orig = 1.502203, loss_refine = 0.662364 [Step 712] loss_orig = 1.502513, loss_refine = -0.660177[Step 712] loss_orig = -0.681088, loss_refine = 0.662897 59%|█████▉ | 713/1208 [9:18:10<7:24:11, 53.84s/it] {'loss': 0.0014, 'grad_norm': 4.525306551249927, 'learning_rate': 4.097682119205298e-07, 'completion_length': 122.79166666666667, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.2916666666666665, 'reward_std': 0.5573514501253763, 'kl': 0.0535888671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 4.72} 59%|█████▉ | 713/1208 [9:18:10<7:24:11, 53.84s/it]Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1486719: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1487592: cache has only 0 modules 59%|█████▉ | 714/1208 [9:18:51<6:51:09, 49.94s/it] {'loss': 0.0016, 'grad_norm': 0.9088014760525729, 'learning_rate': 4.089403973509933e-07, 'completion_length': 101.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.038818359375, 'clip_ratio': 0.0, 'epoch': 4.73} 59%|█████▉ | 714/1208 [9:18:51<6:51:09, 49.94s/it]Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1488465: cache has only 0 modules Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1489338: cache has only 0 modules 59%|█████▉ | 715/1208 [9:19:34<6:32:45, 47.80s/it] {'loss': 0.0007, 'grad_norm': 0.5276180359667647, 'learning_rate': 4.0811258278145694e-07, 'completion_length': 95.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.01861572265625, 'clip_ratio': 0.0, 'epoch': 4.74} 59%|█████▉ | 715/1208 [9:19:34<6:32:45, 47.80s/it]Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1490211: cache has only 0 modules Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1491084: cache has only 0 modules 59%|█████▉ | 716/1208 [9:20:13<6:11:00, 45.25s/it] {'loss': 0.0024, 'grad_norm': 14.635750922821787, 'learning_rate': 4.072847682119205e-07, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.05908203125, 'clip_ratio': 0.0, 'epoch': 4.74} 59%|█████▉ | 716/1208 [9:20:13<6:11:00, 45.25s/it]Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1491957: cache has only 0 modules Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1492830: cache has only 0 modules 59%|█████▉ | 717/1208 [9:20:53<5:55:34, 43.45s/it] {'loss': 0.0022, 'grad_norm': 8.389363128926647, 'learning_rate': 4.064569536423841e-07, 'completion_length': 91.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0557861328125, 'clip_ratio': 0.0, 'epoch': 4.75} 59%|█████▉ | 717/1208 [9:20:53<5:55:34, 43.45s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1493703: cache has only 0 modules Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1494576: cache has only 0 modules 59%|█████▉ | 718/1208 [9:21:21<5:19:00, 39.06s/it] {'loss': 0.0009, 'grad_norm': 0.14213469487478378, 'learning_rate': 4.056291390728477e-07, 'completion_length': 84.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0234375, 'clip_ratio': 0.0, 'epoch': 4.75} 59%|█████▉ | 718/1208 [9:21:21<5:19:00, 39.06s/it]Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1495449: cache has only 0 modules Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1496322: cache has only 0 modules 60%|█████▉ | 719/1208 [9:21:57<5:08:59, 37.91s/it] {'loss': 0.0012, 'grad_norm': 14.192439204506568, 'learning_rate': 4.048013245033112e-07, 'completion_length': 85.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0294189453125, 'clip_ratio': 0.0, 'epoch': 4.76} 60%|█████▉ | 719/1208 [9:21:57<5:08:59, 37.91s/it]Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1497195: cache has only 0 modules Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1498068: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1498941: cache has only 0 modules [Step 719] loss_orig = 0.002131, loss_refine = -0.721807[Step 719] loss_orig = 0.001038, loss_refine = -0.723764 [Step 719] loss_orig = 0.001655, loss_refine = -0.722911[Step 719] loss_orig = 0.001493, loss_refine = -0.723080[Step 719] loss_orig = 0.002431, loss_refine = 1.208973 [Step 719] loss_orig = 0.001598, loss_refine = 1.209217[Step 719] loss_orig = 0.004276, loss_refine = 1.208360 [Step 719] loss_orig = 0.002379, loss_refine = -0.722169 60%|█████▉ | 720/1208 [9:22:50<5:46:45, 42.63s/it] {'loss': 0.0017, 'grad_norm': 10.63611414752773, 'learning_rate': 4.0397350993377485e-07, 'completion_length': 106.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.3506905436515808, 'kl': 0.0478515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 4.77} 60%|█████▉ | 720/1208 [9:22:50<5:46:45, 42.63s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1499814: cache has only 0 modules Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1500687: cache has only 0 modules 60%|█████▉ | 721/1208 [9:23:43<6:10:45, 45.68s/it] {'loss': 0.0017, 'grad_norm': 3.056197546495059, 'learning_rate': 4.0314569536423836e-07, 'completion_length': 111.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.5303300619125366, 'kl': 0.04180908203125, 'clip_ratio': 0.0, 'epoch': 4.77} 60%|█████▉ | 721/1208 [9:23:43<6:10:45, 45.68s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1501560: cache has only 0 modules Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1502433: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'random music'. Your previous answer was [{'action': 'click', 'coordinate': [1025, 598]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1503306: cache has only 0 modules [Step 721] loss_orig = 0.007003, loss_refine = -1.071148[Step 721] loss_orig = 0.001784, loss_refine = 1.082213[Step 721] loss_orig = 0.001765, loss_refine = 0.003415 [Step 721] loss_orig = 0.001667, loss_refine = -1.078197 [Step 721] loss_orig = 0.003017, loss_refine = 0.002768 [Step 721] loss_orig = 0.004874, loss_refine = -1.079022[Step 721] loss_orig = 0.001059, loss_refine = 1.081614 [Step 721] loss_orig = 0.003294, loss_refine = 1.082410 60%|█████▉ | 722/1208 [9:24:37<6:29:22, 48.07s/it] {'loss': 0.0035, 'grad_norm': 6.415069858778655, 'learning_rate': 4.02317880794702e-07, 'completion_length': 90.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.30860670407613117, 'kl': 0.088623046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 4.78} 60%|█████▉ | 722/1208 [9:24:37<6:29:22, 48.07s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1504179: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [408, 83]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1505052: cache has only 0 modules [Step 722] loss_orig = 0.002304, loss_refine = 0.541318[Step 722] loss_orig = 0.001163, loss_refine = 0.541144 [Step 722] loss_orig = 0.000836, loss_refine = 0.541478 [Step 722] loss_orig = 0.001435, loss_refine = 0.541840 [Step 722] loss_orig = 0.002669, loss_refine = 0.541496[Step 722] loss_orig = 0.001372, loss_refine = 0.542868 [Step 722] loss_orig = 0.003516, loss_refine = -1.618310 [Step 722] loss_orig = 0.001312, loss_refine = -1.617094 Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1505925: cache has only 0 modules 60%|█████▉ | 723/1208 [9:25:39<7:03:24, 52.38s/it] {'loss': 0.0014, 'grad_norm': 4.1411868601395785, 'learning_rate': 4.0149006622516556e-07, 'completion_length': 120.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.03594970703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 4.79} 60%|█████▉ | 723/1208 [9:25:39<7:03:24, 52.38s/it]Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1506798: cache has only 0 modules Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1507671: cache has only 0 modules 60%|█████▉ | 724/1208 [9:26:15<6:22:40, 47.44s/it] {'loss': 0.0013, 'grad_norm': 7.22210832745241, 'learning_rate': 4.006622516556291e-07, 'completion_length': 96.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.44403792917728424, 'kl': 0.03192138671875, 'clip_ratio': 0.0, 'epoch': 4.79} 60%|█████▉ | 724/1208 [9:26:15<6:22:40, 47.44s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1508544: cache has only 0 modules Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1509417: cache has only 0 modules 60%|██████ | 725/1208 [9:26:55<6:04:44, 45.31s/it] {'loss': 0.0024, 'grad_norm': 35.438873108305195, 'learning_rate': 3.998344370860927e-07, 'completion_length': 89.5, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.6034669280052185, 'kl': 0.0596923828125, 'clip_ratio': 0.0, 'epoch': 4.8} 60%|██████ | 725/1208 [9:26:55<6:04:44, 45.31s/it]Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1510290: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1511163: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [603, 1407]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1512036: cache has only 0 modules [Step 725] loss_orig = 0.008838, loss_refine = -1.205238[Step 725] loss_orig = 0.001373, loss_refine = 0.726364[Step 725] loss_orig = 0.002101, loss_refine = 0.727907 [Step 725] loss_orig = 0.001634, loss_refine = -1.206291 [Step 725] loss_orig = 0.001920, loss_refine = 0.725563 [Step 725] loss_orig = 0.001751, loss_refine = 0.726137 [Step 725] loss_orig = 0.003189, loss_refine = -1.206149[Step 725] loss_orig = 0.001986, loss_refine = 0.725860 60%|██████ | 726/1208 [9:28:09<7:11:21, 53.70s/it] {'loss': 0.002, 'grad_norm': 5.9007723635570235, 'learning_rate': 3.9900662251655627e-07, 'completion_length': 115.04166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.3268197377522786, 'kl': 0.0638427734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 4.81} 60%|██████ | 726/1208 [9:28:09<7:11:21, 53.70s/it]Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1512909: cache has only 0 modules Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1513782: cache has only 0 modules 60%|██████ | 727/1208 [9:28:40<6:17:05, 47.04s/it] {'loss': 0.0019, 'grad_norm': 4.780178324701347, 'learning_rate': 3.9817880794701984e-07, 'completion_length': 79.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'epoch': 4.81} 60%|██████ | 727/1208 [9:28:40<6:17:05, 47.04s/it]Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1514655: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1515528: cache has only 0 modules 60%|██████ | 728/1208 [9:29:22<6:02:36, 45.33s/it] {'loss': 0.0012, 'grad_norm': 4.709099081181513, 'learning_rate': 3.973509933774834e-07, 'completion_length': 93.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0291748046875, 'clip_ratio': 0.0, 'epoch': 4.82} 60%|██████ | 728/1208 [9:29:22<6:02:36, 45.33s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1516401: cache has only 0 modules Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1517274: cache has only 0 modules 60%|██████ | 729/1208 [9:30:12<6:15:01, 46.98s/it] {'loss': 0.0023, 'grad_norm': 5.95230055542095, 'learning_rate': 3.96523178807947e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.05712890625, 'clip_ratio': 0.0, 'epoch': 4.83} 60%|██████ | 729/1208 [9:30:12<6:15:01, 46.98s/it]Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1518147: cache has only 0 modules Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1519020: cache has only 0 modules 60%|██████ | 730/1208 [9:31:05<6:28:19, 48.74s/it] {'loss': 0.0039, 'grad_norm': 4.544039238791633, 'learning_rate': 3.956953642384106e-07, 'completion_length': 115.4375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.5345224738121033, 'kl': 0.09716796875, 'clip_ratio': 0.0, 'epoch': 4.83} 60%|██████ | 730/1208 [9:31:05<6:28:19, 48.74s/it]Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1519893: cache has only 0 modules Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1520766: cache has only 0 modules 61%|██████ | 731/1208 [9:31:42<5:58:38, 45.11s/it] {'loss': 0.0013, 'grad_norm': 6.1328675429285076, 'learning_rate': 3.9486754966887417e-07, 'completion_length': 89.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03167724609375, 'clip_ratio': 0.0, 'epoch': 4.84} 61%|██████ | 731/1208 [9:31:42<5:58:38, 45.11s/it]Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1521639: cache has only 0 modules Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1522512: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'exchange target and source city'. Your previous answer was [{'action': 'click', 'coordinate': [670, 584]}, {'action': 'click', 'coordinate': [865, 621]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1523385: cache has only 0 modules [Step 731] loss_orig = 0.001265, loss_refine = 0.354766 [Step 731] loss_orig = 0.000747, loss_refine = 0.354314[Step 731] loss_orig = 0.001238, loss_refine = -2.472979 [Step 731] loss_orig = 0.001069, loss_refine = 0.354052 [Step 731] loss_orig = 0.001013, loss_refine = 0.354042[Step 731] loss_orig = 0.000981, loss_refine = 0.354292 [Step 731] loss_orig = 0.000602, loss_refine = 0.355752 [Step 731] loss_orig = 0.001284, loss_refine = 0.354477 61%|██████ | 732/1208 [9:32:36<6:19:49, 47.88s/it] {'loss': 0.0009, 'grad_norm': 7.848021098563059, 'learning_rate': 3.9403973509933774e-07, 'completion_length': 92.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.08333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.3535533845424652, 'kl': 0.02215576171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 4.85} 61%|██████ | 732/1208 [9:32:36<6:19:49, 47.88s/it]Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1524258: cache has only 0 modules Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1525131: cache has only 0 modules 61%|██████ | 733/1208 [9:33:14<5:55:09, 44.86s/it] {'loss': 0.001, 'grad_norm': 11.153787889832092, 'learning_rate': 3.932119205298013e-07, 'completion_length': 86.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02386474609375, 'clip_ratio': 0.0, 'epoch': 4.85} 61%|██████ | 733/1208 [9:33:14<5:55:09, 44.86s/it]Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1526004: cache has only 0 modules Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1526877: cache has only 0 modules 61%|██████ | 734/1208 [9:34:00<5:57:21, 45.23s/it] {'loss': 0.0018, 'grad_norm': 11.192027886291758, 'learning_rate': 3.923841059602649e-07, 'completion_length': 103.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'epoch': 4.86} 61%|██████ | 734/1208 [9:34:00<5:57:21, 45.23s/it]Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1527750: cache has only 0 modules Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1528623: cache has only 0 modules 61%|██████ | 735/1208 [9:34:36<5:34:29, 42.43s/it] {'loss': 0.0012, 'grad_norm': 10.784009506554817, 'learning_rate': 3.9155629139072845e-07, 'completion_length': 95.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.03045654296875, 'clip_ratio': 0.0, 'epoch': 4.87} 61%|██████ | 735/1208 [9:34:36<5:34:29, 42.43s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1529496: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1530369: cache has only 0 modules 61%|██████ | 736/1208 [9:35:06<5:04:39, 38.73s/it] {'loss': 0.0017, 'grad_norm': 8.731341708595272, 'learning_rate': 3.90728476821192e-07, 'completion_length': 75.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.04290771484375, 'clip_ratio': 0.0, 'epoch': 4.87} 61%|██████ | 736/1208 [9:35:06<5:04:39, 38.73s/it]Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1531242: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1532115: cache has only 0 modules 61%|██████ | 737/1208 [9:35:43<4:58:35, 38.04s/it] {'loss': 0.0013, 'grad_norm': 5.771955794542113, 'learning_rate': 3.8990066225165564e-07, 'completion_length': 85.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0313720703125, 'clip_ratio': 0.0, 'epoch': 4.88} 61%|██████ | 737/1208 [9:35:43<4:58:35, 38.04s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1532988: cache has only 0 modules Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1533861: cache has only 0 modules 61%|██████ | 738/1208 [9:36:30<5:20:18, 40.89s/it] {'loss': 0.0013, 'grad_norm': 3.1911777563812573, 'learning_rate': 3.8907284768211916e-07, 'completion_length': 105.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03253173828125, 'clip_ratio': 0.0, 'epoch': 4.89} 61%|██████ | 738/1208 [9:36:30<5:20:18, 40.89s/it]Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1534734: cache has only 0 modules Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1535607: cache has only 0 modules 61%|██████ | 739/1208 [9:37:07<5:09:13, 39.56s/it] {'loss': 0.0026, 'grad_norm': 8.110516041265424, 'learning_rate': 3.882450331125828e-07, 'completion_length': 83.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.064208984375, 'clip_ratio': 0.0, 'epoch': 4.89} 61%|██████ | 739/1208 [9:37:07<5:09:13, 39.56s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1536480: cache has only 0 modules Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1537353: cache has only 0 modules 61%|██████▏ | 740/1208 [9:37:42<5:00:02, 38.47s/it] {'loss': 0.001, 'grad_norm': 7.146874098763703, 'learning_rate': 3.874172185430463e-07, 'completion_length': 84.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0240478515625, 'clip_ratio': 0.0, 'epoch': 4.9} 61%|██████▏ | 740/1208 [9:37:42<5:00:02, 38.47s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1538226: cache has only 0 modules Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1539099: cache has only 0 modules 61%|██████▏ | 741/1208 [9:38:16<4:46:54, 36.86s/it] {'loss': 0.0023, 'grad_norm': 0.2973136706004875, 'learning_rate': 3.865894039735099e-07, 'completion_length': 78.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.056640625, 'clip_ratio': 0.0, 'epoch': 4.91} 61%|██████▏ | 741/1208 [9:38:16<4:46:54, 36.86s/it]Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1539972: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1540845: cache has only 0 modules 61%|██████▏ | 742/1208 [9:38:54<4:49:14, 37.24s/it] {'loss': 0.0021, 'grad_norm': 7.4506623650677914, 'learning_rate': 3.8576158940397355e-07, 'completion_length': 105.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.052978515625, 'clip_ratio': 0.0, 'epoch': 4.91} 61%|██████▏ | 742/1208 [9:38:54<4:49:14, 37.24s/it]Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1541718: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1542591: cache has only 0 modules 62%|██████▏ | 743/1208 [9:39:37<5:02:54, 39.08s/it] {'loss': 0.004, 'grad_norm': 3.601690036497096, 'learning_rate': 3.8493377483443706e-07, 'completion_length': 104.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.1007080078125, 'clip_ratio': 0.0, 'epoch': 4.92} 62%|██████▏ | 743/1208 [9:39:37<5:02:54, 39.08s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1543464: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1544337: cache has only 0 modules 62%|██████▏ | 744/1208 [9:40:19<5:08:25, 39.88s/it] {'loss': 0.0029, 'grad_norm': 5.98521889802514, 'learning_rate': 3.841059602649007e-07, 'completion_length': 94.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0712890625, 'clip_ratio': 0.0, 'epoch': 4.93} 62%|██████▏ | 744/1208 [9:40:19<5:08:25, 39.88s/it]Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1545210: cache has only 0 modules Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1546083: cache has only 0 modules 62%|██████▏ | 745/1208 [9:40:56<5:00:41, 38.97s/it] {'loss': 0.0019, 'grad_norm': 4.991884477833346, 'learning_rate': 3.832781456953642e-07, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0478515625, 'clip_ratio': 0.0, 'epoch': 4.93} 62%|██████▏ | 745/1208 [9:40:56<5:00:41, 38.97s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1546956: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1547829: cache has only 0 modules 62%|██████▏ | 746/1208 [9:41:27<4:42:22, 36.67s/it] {'loss': 0.0009, 'grad_norm': 0.32062810596936986, 'learning_rate': 3.824503311258278e-07, 'completion_length': 78.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0223388671875, 'clip_ratio': 0.0, 'epoch': 4.94} 62%|██████▏ | 746/1208 [9:41:27<4:42:22, 36.67s/it]Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1548702: cache has only 0 modules Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1549575: cache has only 0 modules 62%|██████▏ | 747/1208 [9:42:08<4:51:43, 37.97s/it] {'loss': 0.0047, 'grad_norm': 9.182705680422467, 'learning_rate': 3.8162251655629134e-07, 'completion_length': 99.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.1168212890625, 'clip_ratio': 0.0, 'epoch': 4.95} 62%|██████▏ | 747/1208 [9:42:08<4:51:43, 37.97s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1550448: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1551321: cache has only 0 modules 62%|██████▏ | 748/1208 [9:42:46<4:50:29, 37.89s/it] {'loss': 0.0009, 'grad_norm': 0.48833816204794606, 'learning_rate': 3.8079470198675497e-07, 'completion_length': 91.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02227783203125, 'clip_ratio': 0.0, 'epoch': 4.95} 62%|██████▏ | 748/1208 [9:42:46<4:50:29, 37.89s/it]Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1552194: cache has only 0 modules Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1553067: cache has only 0 modules 62%|██████▏ | 749/1208 [9:43:32<5:09:51, 40.50s/it] {'loss': 0.0022, 'grad_norm': 16.009628184455206, 'learning_rate': 3.7996688741721854e-07, 'completion_length': 99.4375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.7071067541837692, 'kl': 0.05517578125, 'clip_ratio': 0.0, 'epoch': 4.96} 62%|██████▏ | 749/1208 [9:43:32<5:09:51, 40.50s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1553940: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1554813: cache has only 0 modules 62%|██████▏ | 750/1208 [9:44:14<5:11:04, 40.75s/it] {'loss': 0.0017, 'grad_norm': 0.3339211516069006, 'learning_rate': 3.791390728476821e-07, 'completion_length': 90.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'epoch': 4.97} 62%|██████▏ | 750/1208 [9:44:14<5:11:04, 40.75s/it]Start loss calc for inst: click the UI element No /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1555686: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1556559: cache has only 0 modules 62%|██████▏ | 751/1208 [9:45:07<5:38:54, 44.50s/it] {'loss': 0.001, 'grad_norm': 0.19657828934288665, 'learning_rate': 3.783112582781457e-07, 'completion_length': 83.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0257568359375, 'clip_ratio': 0.0, 'epoch': 4.97} 62%|██████▏ | 751/1208 [9:45:07<5:38:54, 44.50s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1557432: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'invert the lens'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1558305: cache has only 0 modules [Step 751] loss_orig = 0.001723, loss_refine = -0.352084[Step 751] loss_orig = 0.001234, loss_refine = -0.352545 [Step 751] loss_orig = 0.001079, loss_refine = -0.350916 [Step 751] loss_orig = 0.004384, loss_refine = -0.352263[Step 751] loss_orig = 0.002179, loss_refine = -0.351755 [Step 751] loss_orig = 0.000866, loss_refine = -0.352332 [Step 751] loss_orig = 0.001376, loss_refine = 2.475374 [Step 751] loss_orig = 0.001980, loss_refine = -0.351776 Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1559178: cache has only 0 modules 62%|██████▏ | 752/1208 [9:46:02<6:02:51, 47.74s/it] {'loss': 0.0013, 'grad_norm': 15.94451616768741, 'learning_rate': 3.7748344370860925e-07, 'completion_length': 94.33333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.23570225636164346, 'kl': 0.038330078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 4.98} 62%|██████▏ | 752/1208 [9:46:02<6:02:51, 47.74s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1560051: cache has only 0 modules Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1560924: cache has only 0 modules 62%|██████▏ | 753/1208 [9:46:36<5:29:35, 43.46s/it] {'loss': 0.0014, 'grad_norm': 3.547246579121906, 'learning_rate': 3.766556291390728e-07, 'completion_length': 93.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0352783203125, 'clip_ratio': 0.0, 'epoch': 4.99} 62%|██████▏ | 753/1208 [9:46:36<5:29:35, 43.46s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1561797: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1562670: cache has only 0 modules [Step 753] loss_orig = 0.000747, loss_refine = 0.937295 [Step 753] loss_orig = 0.001448, loss_refine = 0.936381[Step 753] loss_orig = 0.001862, loss_refine = 0.937390 [Step 753] loss_orig = 0.002515, loss_refine = -0.934196 [Step 753] loss_orig = 0.001377, loss_refine = 0.936788 [Step 753] loss_orig = 0.001428, loss_refine = -0.933576 [Step 753] loss_orig = 0.000801, loss_refine = -0.933407 [Step 753] loss_orig = 0.001478, loss_refine = -0.934180 Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1563543: cache has only 0 modules 62%|██████▏ | 754/1208 [9:47:42<6:21:38, 50.44s/it] {'loss': 0.0026, 'grad_norm': 6.437792544376241, 'learning_rate': 3.758278145695364e-07, 'completion_length': 115.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.33247750997543335, 'kl': 0.0640869140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 4.99} 62%|██████▏ | 754/1208 [9:47:42<6:21:38, 50.44s/it]Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1564416: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1565289: cache has only 0 modules [Step 754] loss_orig = 0.001501, loss_refine = 0.002002[Step 754] loss_orig = 0.001622, loss_refine = 0.001980[Step 754] loss_orig = 0.001470, loss_refine = 0.001680 [Step 754] loss_orig = 0.000977, loss_refine = 0.002578[Step 754] loss_orig = 0.000649, loss_refine = 0.001769 [Step 754] loss_orig = 0.000971, loss_refine = 0.004337 [Step 754] loss_orig = 0.000654, loss_refine = 0.001819 [Step 754] loss_orig = 0.001393, loss_refine = 0.001459 Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.8333333730697632 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1566162: cache has only 0 modules 62%|██████▎ | 755/1208 [9:48:44<6:46:52, 53.89s/it] {'loss': 0.0022, 'grad_norm': 4.986917679019324, 'learning_rate': 3.75e-07, 'completion_length': 94.33333841959636, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2777777910232544, 'rewards/format_reward': 1.0, 'reward': 2.6111111640930176, 'reward_std': 0.11785112818082173, 'kl': 0.04132080078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.0} 62%|██████▎ | 755/1208 [9:48:44<6:46:52, 53.89s/it]Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1567035: cache has only 0 modules Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1567908: cache has only 0 modules 63%|██████▎ | 756/1208 [9:49:21<6:07:23, 48.77s/it] {'loss': 0.0013, 'grad_norm': 8.973748742385133, 'learning_rate': 3.741721854304636e-07, 'completion_length': 87.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'epoch': 5.01} 63%|██████▎ | 756/1208 [9:49:21<6:07:23, 48.77s/it]Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1568781: cache has only 0 modules Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1569654: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1570527: cache has only 0 modules [Step 756] loss_orig = 0.001632, loss_refine = 0.354566[Step 756] loss_orig = 0.001165, loss_refine = -2.473623[Step 756] loss_orig = 0.001463, loss_refine = 0.354155 [Step 756] loss_orig = 0.000850, loss_refine = 0.355016 [Step 756] loss_orig = 0.002781, loss_refine = 0.355889 [Step 756] loss_orig = 0.000936, loss_refine = 0.354498 [Step 756] loss_orig = 0.000785, loss_refine = 0.354345 [Step 756] loss_orig = 0.002100, loss_refine = 0.355153 63%|██████▎ | 757/1208 [9:50:14<6:16:52, 50.14s/it] {'loss': 0.0012, 'grad_norm': 6.772035096255346, 'learning_rate': 3.7334437086092715e-07, 'completion_length': 93.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.03350830078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 5.01} 63%|██████▎ | 757/1208 [9:50:14<6:16:52, 50.14s/it]Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1571400: cache has only 0 modules Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1572273: cache has only 0 modules 63%|██████▎ | 758/1208 [9:51:00<6:04:33, 48.61s/it] {'loss': 0.0017, 'grad_norm': 16.83396154490015, 'learning_rate': 3.725165562913907e-07, 'completion_length': 91.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04345703125, 'clip_ratio': 0.0, 'epoch': 5.02} 63%|██████▎ | 758/1208 [9:51:00<6:04:33, 48.61s/it]Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1573146: cache has only 0 modules Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1574019: cache has only 0 modules 63%|██████▎ | 759/1208 [9:51:34<5:32:01, 44.37s/it] {'loss': 0.0018, 'grad_norm': 4.937645299966845, 'learning_rate': 3.716887417218543e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04437255859375, 'clip_ratio': 0.0, 'epoch': 5.03} 63%|██████▎ | 759/1208 [9:51:34<5:32:01, 44.37s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1574892: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1575765: cache has only 0 modules 63%|██████▎ | 760/1208 [9:52:19<5:33:01, 44.60s/it] {'loss': 0.0028, 'grad_norm': 3.8401151533164404, 'learning_rate': 3.7086092715231786e-07, 'completion_length': 89.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.070556640625, 'clip_ratio': 0.0, 'epoch': 5.03} 63%|██████▎ | 760/1208 [9:52:19<5:33:01, 44.60s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1576638: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. Your previous answer was [{'action': 'click', 'coordinate': [507, 17]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1577511: cache has only 0 modules [Step 760] loss_orig = 0.002287, loss_refine = 0.355282[Step 760] loss_orig = 0.001302, loss_refine = 0.355521[Step 760] loss_orig = 0.001579, loss_refine = 0.355102 [Step 760] loss_orig = 0.000731, loss_refine = 0.354523[Step 760] loss_orig = 0.001874, loss_refine = -2.472780[Step 760] loss_orig = 0.001317, loss_refine = 0.354771 [Step 760] loss_orig = 0.003265, loss_refine = 0.355354 [Step 760] loss_orig = 0.001372, loss_refine = 0.355977 Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1578384: cache has only 0 modules 63%|██████▎ | 761/1208 [9:53:23<6:15:38, 50.42s/it] {'loss': 0.0087, 'grad_norm': 8.992307850814264, 'learning_rate': 3.7003311258278143e-07, 'completion_length': 105.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.27215448021888733, 'kl': 0.21875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 5.04} 63%|██████▎ | 761/1208 [9:53:23<6:15:38, 50.42s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1579257: cache has only 0 modules Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1580130: cache has only 0 modules 63%|██████▎ | 762/1208 [9:54:10<6:05:49, 49.21s/it] {'loss': 0.0016, 'grad_norm': 3.922973422022514, 'learning_rate': 3.69205298013245e-07, 'completion_length': 96.9375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0391845703125, 'clip_ratio': 0.0, 'epoch': 5.05} 63%|██████▎ | 762/1208 [9:54:10<6:05:49, 49.21s/it]Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1581003: cache has only 0 modules Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1581876: cache has only 0 modules 63%|██████▎ | 763/1208 [9:54:43<5:30:15, 44.53s/it] {'loss': 0.0018, 'grad_norm': 0.31352954992847637, 'learning_rate': 3.683774834437086e-07, 'completion_length': 79.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'epoch': 5.05} 63%|██████▎ | 763/1208 [9:54:43<5:30:15, 44.53s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1582749: cache has only 0 modules Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1583622: cache has only 0 modules 63%|██████▎ | 764/1208 [9:55:17<5:05:06, 41.23s/it] {'loss': 0.0022, 'grad_norm': 6.785028440319908, 'learning_rate': 3.6754966887417214e-07, 'completion_length': 90.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0557861328125, 'clip_ratio': 0.0, 'epoch': 5.06} 63%|██████▎ | 764/1208 [9:55:17<5:05:06, 41.23s/it]Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1584495: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1585368: cache has only 0 modules 63%|██████▎ | 765/1208 [9:55:59<5:05:57, 41.44s/it] {'loss': 0.0017, 'grad_norm': 0.905829391140277, 'learning_rate': 3.6672185430463576e-07, 'completion_length': 94.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0423583984375, 'clip_ratio': 0.0, 'epoch': 5.07} 63%|██████▎ | 765/1208 [9:55:59<5:05:57, 41.44s/it]Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1586241: cache has only 0 modules Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1587114: cache has only 0 modules 63%|██████▎ | 766/1208 [9:56:33<4:49:20, 39.28s/it] {'loss': 0.001, 'grad_norm': 0.239461057686223, 'learning_rate': 3.658940397350993e-07, 'completion_length': 85.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0240478515625, 'clip_ratio': 0.0, 'epoch': 5.07} 63%|██████▎ | 766/1208 [9:56:33<4:49:20, 39.28s/it]Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1587987: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1588860: cache has only 0 modules [Step 766] loss_orig = 0.001637, loss_refine = -0.723321[Step 766] loss_orig = 0.001365, loss_refine = -0.721749[Step 766] loss_orig = 0.001746, loss_refine = 1.208703 [Step 766] loss_orig = 0.002859, loss_refine = -0.722389[Step 766] loss_orig = 0.000897, loss_refine = -0.723695 [Step 766] loss_orig = 0.001578, loss_refine = -0.722681 [Step 766] loss_orig = 0.001099, loss_refine = 1.208948 [Step 766] loss_orig = 0.001085, loss_refine = 1.208591 Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1589733: cache has only 0 modules 63%|██████▎ | 767/1208 [9:57:31<5:31:18, 45.08s/it] {'loss': 0.0013, 'grad_norm': 7.0541438619178995, 'learning_rate': 3.650662251655629e-07, 'completion_length': 104.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.03302001953125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 5.08} 63%|██████▎ | 767/1208 [9:57:31<5:31:18, 45.08s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1590606: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'random music'. Your previous answer was [{'action': 'click', 'coordinate': [1024, 581]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1591479: cache has only 0 modules [Step 767] loss_orig = 0.001614, loss_refine = -0.352423[Step 767] loss_orig = 0.004855, loss_refine = -0.352086 [Step 767] loss_orig = 0.001887, loss_refine = -0.351865[Step 767] loss_orig = 0.004243, loss_refine = -0.350479 [Step 767] loss_orig = 0.002303, loss_refine = -0.351300 [Step 767] loss_orig = 0.002223, loss_refine = 2.476552[Step 767] loss_orig = 0.003392, loss_refine = -0.351884 [Step 767] loss_orig = 0.001709, loss_refine = -0.352152 Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1592352: cache has only 0 modules 64%|██████▎ | 768/1208 [9:58:27<5:52:46, 48.11s/it] {'loss': 0.0023, 'grad_norm': 10.548982833727097, 'learning_rate': 3.642384105960264e-07, 'completion_length': 90.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.070556640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 5.09} 64%|██████▎ | 768/1208 [9:58:27<5:52:46, 48.11s/it]Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1593225: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1594098: cache has only 0 modules [Step 768] loss_orig = 0.002356, loss_refine = -0.502331[Step 768] loss_orig = 0.001001, loss_refine = -0.502518 [Step 768] loss_orig = 0.001085, loss_refine = -0.502735[Step 768] loss_orig = 0.002135, loss_refine = 0.841679 [Step 768] loss_orig = 0.001901, loss_refine = -0.502577 [Step 768] loss_orig = 0.001649, loss_refine = -0.502629 [Step 768] loss_orig = 0.001494, loss_refine = 2.187694 [Step 768] loss_orig = 0.001258, loss_refine = -0.501354 Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1594971: cache has only 0 modules 64%|██████▎ | 769/1208 [9:59:25<6:14:31, 51.19s/it] {'loss': 0.0027, 'grad_norm': 3.7491450878017822, 'learning_rate': 3.6341059602649004e-07, 'completion_length': 94.41666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.24800793329874674, 'kl': 0.0640869140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 5.09} 64%|██████▎ | 769/1208 [9:59:25<6:14:31, 51.19s/it]Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1595844: cache has only 0 modules Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1596717: cache has only 0 modules 64%|██████▎ | 770/1208 [10:00:03<5:44:06, 47.14s/it] {'loss': 0.001, 'grad_norm': 0.3066160021907757, 'learning_rate': 3.6258278145695367e-07, 'completion_length': 89.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0247802734375, 'clip_ratio': 0.0, 'epoch': 5.1} 64%|██████▎ | 770/1208 [10:00:03<5:44:06, 47.14s/it]Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1597590: cache has only 0 modules Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1598463: cache has only 0 modules 64%|██████▍ | 771/1208 [10:00:38<5:16:26, 43.45s/it] {'loss': 0.0034, 'grad_norm': 9.155351077188692, 'learning_rate': 3.617549668874172e-07, 'completion_length': 94.625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.5303300768136978, 'kl': 0.08587646484375, 'clip_ratio': 0.0, 'epoch': 5.11} 64%|██████▍ | 771/1208 [10:00:38<5:16:26, 43.45s/it]Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1599336: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1600209: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1601082: cache has only 0 modules [Step 771] loss_orig = 0.003249, loss_refine = 0.937385 [Step 771] loss_orig = 0.002167, loss_refine = -0.933450[Step 771] loss_orig = 0.002293, loss_refine = 0.938273 [Step 771] loss_orig = 0.002329, loss_refine = -0.933026 [Step 771] loss_orig = 0.001990, loss_refine = 0.937224 [Step 771] loss_orig = 0.002123, loss_refine = -0.933587 [Step 771] loss_orig = 0.003066, loss_refine = 0.937945 [Step 771] loss_orig = 0.002557, loss_refine = -0.930999 64%|██████▍ | 772/1208 [10:01:39<5:54:15, 48.75s/it] {'loss': 0.0022, 'grad_norm': 7.566726453803236, 'learning_rate': 3.609271523178808e-07, 'completion_length': 106.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.3506905436515808, 'kl': 0.054443359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.11} 64%|██████▍ | 772/1208 [10:01:39<5:54:15, 48.75s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1601955: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1602828: cache has only 0 modules 64%|██████▍ | 773/1208 [10:02:11<5:18:31, 43.93s/it] {'loss': 0.0017, 'grad_norm': 15.645712028837622, 'learning_rate': 3.600993377483443e-07, 'completion_length': 87.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'epoch': 5.12} 64%|██████▍ | 773/1208 [10:02:11<5:18:31, 43.93s/it]Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1603701: cache has only 0 modules Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1604574: cache has only 0 modules 64%|██████▍ | 774/1208 [10:03:01<5:30:26, 45.68s/it] {'loss': 0.0014, 'grad_norm': 9.810372692198813, 'learning_rate': 3.5927152317880795e-07, 'completion_length': 102.625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.5303300619125366, 'kl': 0.03466796875, 'clip_ratio': 0.0, 'epoch': 5.13} 64%|██████▍ | 774/1208 [10:03:01<5:30:26, 45.68s/it]Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1605447: cache has only 0 modules Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1606320: cache has only 0 modules 64%|██████▍ | 775/1208 [10:03:48<5:33:09, 46.17s/it] {'loss': 0.0015, 'grad_norm': 9.24672407387258, 'learning_rate': 3.584437086092715e-07, 'completion_length': 99.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.03704833984375, 'clip_ratio': 0.0, 'epoch': 5.13} 64%|██████▍ | 775/1208 [10:03:48<5:33:09, 46.17s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1607193: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [552, 190]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1608066: cache has only 0 modules [Step 775] loss_orig = 0.001927, loss_refine = 0.196379[Step 775] loss_orig = 0.002826, loss_refine = 0.196007[Step 775] loss_orig = 0.002810, loss_refine = 0.196388[Step 775] loss_orig = 0.002245, loss_refine = 1.757207[Step 775] loss_orig = 0.002199, loss_refine = 0.196585 [Step 775] loss_orig = 0.001016, loss_refine = -1.364018 [Step 775] loss_orig = 0.002570, loss_refine = 0.196348 [Step 775] loss_orig = 0.002416, loss_refine = -1.363733 Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1608939: cache has only 0 modules 64%|██████▍ | 776/1208 [10:04:51<6:06:55, 50.96s/it] {'loss': 0.0035, 'grad_norm': 14.31841480178341, 'learning_rate': 3.576158940397351e-07, 'completion_length': 104.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.3314744532108307, 'kl': 0.097412109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 5.14} 64%|██████▍ | 776/1208 [10:04:51<6:06:55, 50.96s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1609812: cache has only 0 modules Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1610685: cache has only 0 modules 64%|██████▍ | 777/1208 [10:05:24<5:28:54, 45.79s/it] {'loss': 0.0018, 'grad_norm': 5.726165081747432, 'learning_rate': 3.5678807947019866e-07, 'completion_length': 83.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.045654296875, 'clip_ratio': 0.0, 'epoch': 5.15} 64%|██████▍ | 777/1208 [10:05:24<5:28:54, 45.79s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1611558: cache has only 0 modules Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1612431: cache has only 0 modules 64%|██████▍ | 778/1208 [10:06:05<5:16:42, 44.19s/it] {'loss': 0.0024, 'grad_norm': 8.170059754390623, 'learning_rate': 3.5596026490066223e-07, 'completion_length': 89.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0601806640625, 'clip_ratio': 0.0, 'epoch': 5.15} 64%|██████▍ | 778/1208 [10:06:05<5:16:42, 44.19s/it]Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1613304: cache has only 0 modules Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1614177: cache has only 0 modules 64%|██████▍ | 779/1208 [10:06:50<5:18:52, 44.60s/it] {'loss': 0.0015, 'grad_norm': 5.046835655836901, 'learning_rate': 3.551324503311258e-07, 'completion_length': 101.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03863525390625, 'clip_ratio': 0.0, 'epoch': 5.16} 64%|██████▍ | 779/1208 [10:06:50<5:18:52, 44.60s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1615050: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1615923: cache has only 0 modules 65%|██████▍ | 780/1208 [10:07:28<5:04:15, 42.65s/it] {'loss': 0.0028, 'grad_norm': 18.890531895771325, 'learning_rate': 3.5430463576158937e-07, 'completion_length': 94.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.0699462890625, 'clip_ratio': 0.0, 'epoch': 5.17} 65%|██████▍ | 780/1208 [10:07:28<5:04:15, 42.65s/it]Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1616796: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1617669: cache has only 0 modules 65%|██████▍ | 781/1208 [10:08:04<4:48:38, 40.56s/it] {'loss': 0.0014, 'grad_norm': 12.968081034256983, 'learning_rate': 3.53476821192053e-07, 'completion_length': 86.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.035888671875, 'clip_ratio': 0.0, 'epoch': 5.17} 65%|██████▍ | 781/1208 [10:08:04<4:48:38, 40.56s/it]Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1618542: cache has only 0 modules Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1619415: cache has only 0 modules 65%|██████▍ | 782/1208 [10:08:43<4:45:18, 40.18s/it] {'loss': 0.0022, 'grad_norm': 0.43970694368693025, 'learning_rate': 3.5264900662251656e-07, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0543212890625, 'clip_ratio': 0.0, 'epoch': 5.18} 65%|██████▍ | 782/1208 [10:08:43<4:45:18, 40.18s/it]Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1620288: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1621161: cache has only 0 modules 65%|██████▍ | 783/1208 [10:09:34<5:06:51, 43.32s/it] {'loss': 0.0022, 'grad_norm': 5.346159766979586, 'learning_rate': 3.5182119205298013e-07, 'completion_length': 108.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0555419921875, 'clip_ratio': 0.0, 'epoch': 5.19} 65%|██████▍ | 783/1208 [10:09:34<5:06:51, 43.32s/it]Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1622034: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_ArrowCircle_M'. Your previous answer was [{'action': 'click', 'coordinate': [345, 918]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1622907: cache has only 0 modules [Step 783] loss_orig = 0.006938, loss_refine = -0.350723[Step 783] loss_orig = 0.001800, loss_refine = -0.351563[Step 783] loss_orig = 0.001014, loss_refine = -0.351640[Step 783] loss_orig = 0.002323, loss_refine = 2.475991 [Step 783] loss_orig = 0.002530, loss_refine = -0.351181[Step 783] loss_orig = 0.004516, loss_refine = -0.350654[Step 783] loss_orig = 0.001410, loss_refine = -0.350496 [Step 783] loss_orig = 0.004239, loss_refine = -0.351496 Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1623780: cache has only 0 modules 65%|██████▍ | 784/1208 [10:10:30<5:33:50, 47.24s/it] {'loss': 0.0016, 'grad_norm': 18.064758398488586, 'learning_rate': 3.509933774834437e-07, 'completion_length': 97.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.9166666666666665, 'reward_std': 0.23570225636164346, 'kl': 0.04925537109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 5.19} 65%|██████▍ | 784/1208 [10:10:30<5:33:50, 47.24s/it]Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1624653: cache has only 0 modules Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1625526: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1626399: cache has only 0 modules [Step 784] loss_orig = 0.002598, loss_refine = 0.001566[Step 784] loss_orig = 0.001277, loss_refine = 0.001800[Step 784] loss_orig = 0.002632, loss_refine = 0.001426[Step 784] loss_orig = 0.000927, loss_refine = 0.001469 [Step 784] loss_orig = 0.001430, loss_refine = 0.001585 [Step 784] loss_orig = 0.000764, loss_refine = 0.001348 [Step 784] loss_orig = 0.001952, loss_refine = 0.001927 [Step 784] loss_orig = 0.001870, loss_refine = 0.001774 65%|██████▍ | 785/1208 [10:11:24<5:46:43, 49.18s/it] {'loss': 0.0014, 'grad_norm': 0.17537843305838377, 'learning_rate': 3.5016556291390727e-07, 'completion_length': 99.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.03460693359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 5.2} 65%|██████▍ | 785/1208 [10:11:24<5:46:43, 49.18s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1627272: cache has only 0 modules Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1628145: cache has only 0 modules 65%|██████▌ | 786/1208 [10:12:04<5:26:37, 46.44s/it] {'loss': 0.0011, 'grad_norm': 32.137039296179275, 'learning_rate': 3.4933774834437084e-07, 'completion_length': 89.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.02752685546875, 'clip_ratio': 0.0, 'epoch': 5.21} 65%|██████▌ | 786/1208 [10:12:04<5:26:37, 46.44s/it]Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1629018: cache has only 0 modules Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1629891: cache has only 0 modules 65%|██████▌ | 787/1208 [10:12:40<5:02:50, 43.16s/it] {'loss': 0.0023, 'grad_norm': 8.175219798961932, 'learning_rate': 3.485099337748344e-07, 'completion_length': 80.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0579833984375, 'clip_ratio': 0.0, 'epoch': 5.21} 65%|██████▌ | 787/1208 [10:12:40<5:02:50, 43.16s/it]Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1630764: cache has only 0 modules Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1631637: cache has only 0 modules 65%|██████▌ | 788/1208 [10:13:21<4:58:32, 42.65s/it] {'loss': 0.0031, 'grad_norm': 8.93224194660249, 'learning_rate': 3.47682119205298e-07, 'completion_length': 103.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0771484375, 'clip_ratio': 0.0, 'epoch': 5.22} 65%|██████▌ | 788/1208 [10:13:21<4:58:32, 42.65s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1632510: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1633383: cache has only 0 modules 65%|██████▌ | 789/1208 [10:14:02<4:54:58, 42.24s/it] {'loss': 0.0021, 'grad_norm': 7.991030590624166, 'learning_rate': 3.468543046357616e-07, 'completion_length': 88.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.0513916015625, 'clip_ratio': 0.0, 'epoch': 5.23} 65%|██████▌ | 789/1208 [10:14:02<4:54:58, 42.24s/it]Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1634256: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1635129: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1636002: cache has only 0 modules [Step 789] loss_orig = 0.003643, loss_refine = -0.774829[Step 789] loss_orig = 0.001559, loss_refine = 1.005075 [Step 789] loss_orig = 0.001087, loss_refine = -0.773391[Step 789] loss_orig = 0.001287, loss_refine = -0.775107 [Step 789] loss_orig = 0.000540, loss_refine = -0.775379 [Step 789] loss_orig = 0.001926, loss_refine = 0.113311 [Step 789] loss_orig = 0.003069, loss_refine = 0.112414 [Step 789] loss_orig = 0.000946, loss_refine = 1.888240 65%|██████▌ | 790/1208 [10:15:12<5:51:44, 50.49s/it] {'loss': 0.0016, 'grad_norm': 21.817909324882663, 'learning_rate': 3.460264900662251e-07, 'completion_length': 102.04166666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.3753305673599243, 'kl': 0.03131103515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.23} 65%|██████▌ | 790/1208 [10:15:12<5:51:44, 50.49s/it]Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1636875: cache has only 0 modules Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1637748: cache has only 0 modules 65%|██████▌ | 791/1208 [10:15:58<5:40:31, 49.00s/it] {'loss': 0.0019, 'grad_norm': 6.764544200734016, 'learning_rate': 3.4519867549668874e-07, 'completion_length': 102.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0478515625, 'clip_ratio': 0.0, 'epoch': 5.24} 65%|██████▌ | 791/1208 [10:15:58<5:40:31, 49.00s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1638621: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1639494: cache has only 0 modules [Step 791] loss_orig = 0.002329, loss_refine = 0.726379[Step 791] loss_orig = 0.001455, loss_refine = 0.726302[Step 791] loss_orig = 0.001770, loss_refine = 0.726758[Step 791] loss_orig = 0.003257, loss_refine = -1.204832 [Step 791] loss_orig = 0.001314, loss_refine = 0.726362 [Step 791] loss_orig = 0.002563, loss_refine = -1.205993 [Step 791] loss_orig = 0.001749, loss_refine = 0.726082 [Step 791] loss_orig = 0.001829, loss_refine = -1.205701 Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1640367: cache has only 0 modules 66%|██████▌ | 792/1208 [10:16:58<6:03:47, 52.47s/it] {'loss': 0.0026, 'grad_norm': 6.049511778498247, 'learning_rate': 3.4437086092715226e-07, 'completion_length': 101.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.066650390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 5.25} 66%|██████▌ | 792/1208 [10:16:58<6:03:47, 52.47s/it]Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1641240: cache has only 0 modules Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1642113: cache has only 0 modules 66%|██████▌ | 793/1208 [10:17:35<5:31:01, 47.86s/it] {'loss': 0.0017, 'grad_norm': 19.381649950397073, 'learning_rate': 3.435430463576159e-07, 'completion_length': 83.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 5.25} 66%|██████▌ | 793/1208 [10:17:35<5:31:01, 47.86s/it]Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1642986: cache has only 0 modules Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1643859: cache has only 0 modules 66%|██████▌ | 794/1208 [10:18:21<5:25:22, 47.15s/it] {'loss': 0.0015, 'grad_norm': 0.2175561924692853, 'learning_rate': 3.4271523178807945e-07, 'completion_length': 90.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0372314453125, 'clip_ratio': 0.0, 'epoch': 5.26} 66%|██████▌ | 794/1208 [10:18:21<5:25:22, 47.15s/it]Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1644732: cache has only 0 modules Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1645605: cache has only 0 modules 66%|██████▌ | 795/1208 [10:18:58<5:03:39, 44.11s/it] {'loss': 0.0033, 'grad_norm': 11.105855039841524, 'learning_rate': 3.41887417218543e-07, 'completion_length': 83.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0814208984375, 'clip_ratio': 0.0, 'epoch': 5.26} 66%|██████▌ | 795/1208 [10:18:58<5:03:39, 44.11s/it]Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1646478: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1647351: cache has only 0 modules [Step 795] loss_orig = 0.001670, loss_refine = -0.658634 [Step 795] loss_orig = 0.000824, loss_refine = 0.662685[Step 795] loss_orig = 0.000628, loss_refine = 0.663430 [Step 795] loss_orig = 0.001301, loss_refine = 0.663695 [Step 795] loss_orig = 0.000716, loss_refine = 0.662870 [Step 795] loss_orig = 0.001063, loss_refine = 0.662557 [Step 795] loss_orig = 0.000841, loss_refine = -0.656587 [Step 795] loss_orig = 0.000890, loss_refine = -1.983242 Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1648224: cache has only 0 modules 66%|██████▌ | 796/1208 [10:19:54<5:28:39, 47.86s/it] {'loss': 0.0025, 'grad_norm': 36.87814077806756, 'learning_rate': 3.4105960264900665e-07, 'completion_length': 98.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.20833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.43015046914418537, 'kl': 0.04754638671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 5.27} 66%|██████▌ | 796/1208 [10:19:54<5:28:39, 47.86s/it]Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1649097: cache has only 0 modules Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1649970: cache has only 0 modules 66%|██████▌ | 797/1208 [10:20:35<5:12:17, 45.59s/it] {'loss': 0.0017, 'grad_norm': 0.3181062877726, 'learning_rate': 3.4023178807947016e-07, 'completion_length': 92.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0413818359375, 'clip_ratio': 0.0, 'epoch': 5.28} 66%|██████▌ | 797/1208 [10:20:35<5:12:17, 45.59s/it]Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1650843: cache has only 0 modules Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1651716: cache has only 0 modules 66%|██████▌ | 798/1208 [10:21:09<4:48:48, 42.26s/it] {'loss': 0.0025, 'grad_norm': 0.40400965432661523, 'learning_rate': 3.394039735099338e-07, 'completion_length': 78.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.061279296875, 'clip_ratio': 0.0, 'epoch': 5.28} 66%|██████▌ | 798/1208 [10:21:09<4:48:48, 42.26s/it]Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1652589: cache has only 0 modules Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1653462: cache has only 0 modules 66%|██████▌ | 799/1208 [10:21:51<4:47:15, 42.14s/it] {'loss': 0.0015, 'grad_norm': 7.937084847020051, 'learning_rate': 3.385761589403973e-07, 'completion_length': 93.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.036376953125, 'clip_ratio': 0.0, 'epoch': 5.29} 66%|██████▌ | 799/1208 [10:21:51<4:47:15, 42.14s/it]Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1654335: cache has only 0 modules Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1655208: cache has only 0 modules 66%|██████▌ | 800/1208 [10:22:24<4:28:41, 39.51s/it] {'loss': 0.0023, 'grad_norm': 8.522482842479931, 'learning_rate': 3.3774834437086093e-07, 'completion_length': 88.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.057861328125, 'clip_ratio': 0.0, 'epoch': 5.3} 66%|██████▌ | 800/1208 [10:22:25<4:28:41, 39.51s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1656081: cache has only 0 modules Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1656954: cache has only 0 modules 66%|██████▋ | 801/1208 [10:23:10<4:39:58, 41.27s/it] {'loss': 0.0065, 'grad_norm': 7.257711068610803, 'learning_rate': 3.369205298013245e-07, 'completion_length': 99.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.163330078125, 'clip_ratio': 0.0, 'epoch': 5.3} 66%|██████▋ | 801/1208 [10:23:10<4:39:58, 41.27s/it]Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1657827: cache has only 0 modules Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1658700: cache has only 0 modules 66%|██████▋ | 802/1208 [10:23:52<4:41:29, 41.60s/it] {'loss': 0.0011, 'grad_norm': 10.094992455707416, 'learning_rate': 3.3609271523178807e-07, 'completion_length': 83.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.027099609375, 'clip_ratio': 0.0, 'epoch': 5.31} 66%|██████▋ | 802/1208 [10:23:52<4:41:29, 41.60s/it]Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1659573: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1660446: cache has only 0 modules 66%|██████▋ | 803/1208 [10:24:32<4:37:17, 41.08s/it] {'loss': 0.0018, 'grad_norm': 10.424172556578748, 'learning_rate': 3.3526490066225164e-07, 'completion_length': 98.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04425048828125, 'clip_ratio': 0.0, 'epoch': 5.32} 66%|██████▋ | 803/1208 [10:24:32<4:37:17, 41.08s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1661319: cache has only 0 modules Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1662192: cache has only 0 modules 67%|██████▋ | 804/1208 [10:25:10<4:29:22, 40.01s/it] {'loss': 0.0007, 'grad_norm': 0.21151045641213786, 'learning_rate': 3.344370860927152e-07, 'completion_length': 86.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.01861572265625, 'clip_ratio': 0.0, 'epoch': 5.32} 67%|██████▋ | 804/1208 [10:25:10<4:29:22, 40.01s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1663065: cache has only 0 modules Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1663938: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1664811: cache has only 0 modules [Step 804] loss_orig = 0.001081, loss_refine = -0.538369[Step 804] loss_orig = 0.001237, loss_refine = -0.537266 [Step 804] loss_orig = 0.001175, loss_refine = -0.539028[Step 804] loss_orig = 0.001135, loss_refine = -0.538443[Step 804] loss_orig = 0.002312, loss_refine = 1.621908 [Step 804] loss_orig = 0.001445, loss_refine = -0.538656 [Step 804] loss_orig = 0.002872, loss_refine = -0.536482 [Step 804] loss_orig = 0.001063, loss_refine = 1.621051 67%|██████▋ | 805/1208 [10:26:08<5:06:44, 45.67s/it] {'loss': 0.0014, 'grad_norm': 4.226900171062667, 'learning_rate': 3.336092715231788e-07, 'completion_length': 106.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.03240966796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 5.33} 67%|██████▋ | 805/1208 [10:26:08<5:06:44, 45.67s/it]Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1665684: cache has only 0 modules Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1666557: cache has only 0 modules 67%|██████▋ | 806/1208 [10:26:50<4:57:56, 44.47s/it] {'loss': 0.0012, 'grad_norm': 0.34190310834572807, 'learning_rate': 3.3278145695364235e-07, 'completion_length': 97.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0303955078125, 'clip_ratio': 0.0, 'epoch': 5.34} 67%|██████▋ | 806/1208 [10:26:50<4:57:56, 44.47s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1667430: cache has only 0 modules Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1668303: cache has only 0 modules 67%|██████▋ | 807/1208 [10:27:26<4:39:20, 41.80s/it] {'loss': 0.001, 'grad_norm': 0.17432101941243774, 'learning_rate': 3.3195364238410597e-07, 'completion_length': 89.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02423095703125, 'clip_ratio': 0.0, 'epoch': 5.34} 67%|██████▋ | 807/1208 [10:27:26<4:39:20, 41.80s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1669176: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1670049: cache has only 0 modules 67%|██████▋ | 808/1208 [10:28:02<4:28:25, 40.26s/it] {'loss': 0.003, 'grad_norm': 7.55345981954425, 'learning_rate': 3.3112582781456954e-07, 'completion_length': 86.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0750732421875, 'clip_ratio': 0.0, 'epoch': 5.35} 67%|██████▋ | 808/1208 [10:28:02<4:28:25, 40.26s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1670922: cache has only 0 modules Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1671795: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1672668: cache has only 0 modules [Step 808] loss_orig = 0.002094, loss_refine = 0.001855[Step 808] loss_orig = 0.000756, loss_refine = 0.004664 [Step 808] loss_orig = 0.000459, loss_refine = 0.002928 [Step 808] loss_orig = 0.001640, loss_refine = 0.003479 [Step 808] loss_orig = 0.000992, loss_refine = 0.000929 [Step 808] loss_orig = 0.000593, loss_refine = 0.004771 [Step 808] loss_orig = 0.002970, loss_refine = 0.004061 [Step 808] loss_orig = 0.001038, loss_refine = 0.000752 67%|██████▋ | 809/1208 [10:29:02<5:07:11, 46.19s/it] {'loss': 0.0022, 'grad_norm': 4.117494062502806, 'learning_rate': 3.302980132450331e-07, 'completion_length': 91.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.36} 67%|██████▋ | 809/1208 [10:29:02<5:07:11, 46.19s/it]Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1673541: cache has only 0 modules Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1674414: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'invert the lens'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1675287: cache has only 0 modules [Step 809] loss_orig = -0.352680, loss_refine = -0.536623[Step 809] loss_orig = -0.352049, loss_refine = -0.538039 [Step 809] loss_orig = -0.352600, loss_refine = -0.538376 [Step 809] loss_orig = -0.352031, loss_refine = 1.621778[Step 809] loss_orig = -0.352169, loss_refine = 1.624986 [Step 809] loss_orig = -0.351920, loss_refine = -0.537662 [Step 809] loss_orig = 2.475543, loss_refine = -0.538026 [Step 809] loss_orig = -0.352718, loss_refine = -0.538311 67%|██████▋ | 810/1208 [10:30:15<5:58:17, 54.01s/it] {'loss': 0.0028, 'grad_norm': 25.265522183179105, 'learning_rate': 3.294701986754967e-07, 'completion_length': 106.25, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4166666666666665, 'reward_std': 0.5443089604377747, 'kl': 0.053955078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 5.36} 67%|██████▋ | 810/1208 [10:30:15<5:58:17, 54.01s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1676160: cache has only 0 modules Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1677033: cache has only 0 modules 67%|██████▋ | 811/1208 [10:30:55<5:29:55, 49.86s/it] {'loss': 0.0017, 'grad_norm': 4.001418301563214, 'learning_rate': 3.2864238410596025e-07, 'completion_length': 97.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0435791015625, 'clip_ratio': 0.0, 'epoch': 5.37} 67%|██████▋ | 811/1208 [10:30:55<5:29:55, 49.86s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1677906: cache has only 0 modules Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1678779: cache has only 0 modules 67%|██████▋ | 812/1208 [10:31:35<5:10:05, 46.98s/it] {'loss': 0.0023, 'grad_norm': 6.822219195725855, 'learning_rate': 3.278145695364238e-07, 'completion_length': 81.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0572509765625, 'clip_ratio': 0.0, 'epoch': 5.38} 67%|██████▋ | 812/1208 [10:31:35<5:10:05, 46.98s/it]Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1679652: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1680525: cache has only 0 modules 67%|██████▋ | 813/1208 [10:32:12<4:49:25, 43.96s/it] {'loss': 0.0029, 'grad_norm': 7.7951304932694745, 'learning_rate': 3.269867549668874e-07, 'completion_length': 90.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0732421875, 'clip_ratio': 0.0, 'epoch': 5.38} 67%|██████▋ | 813/1208 [10:32:12<4:49:25, 43.96s/it]Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1681398: cache has only 0 modules Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1682271: cache has only 0 modules 67%|██████▋ | 814/1208 [10:32:50<4:36:15, 42.07s/it] {'loss': 0.0019, 'grad_norm': 8.307076610048606, 'learning_rate': 3.2615894039735096e-07, 'completion_length': 84.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.37796446681022644, 'kl': 0.04840087890625, 'clip_ratio': 0.0, 'epoch': 5.39} 67%|██████▋ | 814/1208 [10:32:50<4:36:15, 42.07s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1683144: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1299, 458]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1684017: cache has only 0 modules [Step 814] loss_orig = 0.000838, loss_refine = -0.502063 [Step 814] loss_orig = 0.001662, loss_refine = -1.845703 [Step 814] loss_orig = 0.001071, loss_refine = 0.840961 [Step 814] loss_orig = 0.001304, loss_refine = 0.841456[Step 814] loss_orig = 0.000920, loss_refine = 0.841083[Step 814] loss_orig = 0.000674, loss_refine = -0.502778 [Step 814] loss_orig = 0.001091, loss_refine = -0.502519 [Step 814] loss_orig = 0.000563, loss_refine = 0.841269 Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1684890: cache has only 0 modules 67%|██████▋ | 815/1208 [10:33:57<5:24:32, 49.55s/it] {'loss': 0.0018, 'grad_norm': 6.713547417280794, 'learning_rate': 3.253311258278146e-07, 'completion_length': 106.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.24800793329874674, 'kl': 0.0399169921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.4} 67%|██████▋ | 815/1208 [10:33:57<5:24:32, 49.55s/it]Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1685763: cache has only 0 modules Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1686636: cache has only 0 modules 68%|██████▊ | 816/1208 [10:34:33<4:57:36, 45.55s/it] {'loss': 0.0025, 'grad_norm': 0.9785685792110543, 'learning_rate': 3.245033112582781e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.06121826171875, 'clip_ratio': 0.0, 'epoch': 5.4} 68%|██████▊ | 816/1208 [10:34:33<4:57:36, 45.55s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1687509: cache has only 0 modules Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1688382: cache has only 0 modules 68%|██████▊ | 817/1208 [10:35:10<4:39:19, 42.86s/it] {'loss': 0.003, 'grad_norm': 4.573148105094418, 'learning_rate': 3.236754966887417e-07, 'completion_length': 81.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.07391357421875, 'clip_ratio': 0.0, 'epoch': 5.41} 68%|██████▊ | 817/1208 [10:35:10<4:39:19, 42.86s/it]Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1689255: cache has only 0 modules Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1690128: cache has only 0 modules 68%|██████▊ | 818/1208 [10:35:51<4:36:25, 42.53s/it] {'loss': 0.0015, 'grad_norm': 7.7761474526218635, 'learning_rate': 3.2284768211920524e-07, 'completion_length': 95.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0377197265625, 'clip_ratio': 0.0, 'epoch': 5.42} 68%|██████▊ | 818/1208 [10:35:51<4:36:25, 42.53s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1691001: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'flod this content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1691874: cache has only 0 modules [Step 818] loss_orig = 0.002524, loss_refine = -0.932626 [Step 818] loss_orig = 0.001354, loss_refine = 0.936448 [Step 818] loss_orig = 0.001656, loss_refine = -0.933968 [Step 818] loss_orig = 0.001349, loss_refine = 0.938432 [Step 818] loss_orig = 0.001534, loss_refine = -0.933460 [Step 818] loss_orig = 0.002294, loss_refine = -0.922450 [Step 818] loss_orig = 0.000794, loss_refine = 0.936301 [Step 818] loss_orig = 0.001349, loss_refine = 0.936349 Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1692747: cache has only 0 modules 68%|██████▊ | 819/1208 [10:37:05<5:35:58, 51.82s/it] {'loss': 0.002, 'grad_norm': 8.06142432746446, 'learning_rate': 3.2201986754966886e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.02972412109375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.42} 68%|██████▊ | 819/1208 [10:37:05<5:35:58, 51.82s/it]Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1693620: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1694493: cache has only 0 modules 68%|██████▊ | 820/1208 [10:37:43<5:08:14, 47.67s/it] {'loss': 0.0016, 'grad_norm': 0.336108686943217, 'learning_rate': 3.2119205298013243e-07, 'completion_length': 83.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.040771484375, 'clip_ratio': 0.0, 'epoch': 5.43} 68%|██████▊ | 820/1208 [10:37:43<5:08:14, 47.67s/it]Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1695366: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1696239: cache has only 0 modules [Step 820] loss_orig = 0.002365, loss_refine = 1.211637[Step 820] loss_orig = 0.001547, loss_refine = -0.722606[Step 820] loss_orig = 0.002585, loss_refine = -0.722719 [Step 820] loss_orig = 0.001527, loss_refine = 1.212156 [Step 820] loss_orig = 0.001510, loss_refine = -0.722434 [Step 820] loss_orig = 0.004322, loss_refine = -0.716585 [Step 820] loss_orig = 0.000999, loss_refine = -0.722379 [Step 820] loss_orig = 0.002997, loss_refine = 1.208795 Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1697112: cache has only 0 modules 68%|██████▊ | 821/1208 [10:38:44<5:33:07, 51.65s/it] {'loss': 0.0023, 'grad_norm': 7.278799279817368, 'learning_rate': 3.20364238410596e-07, 'completion_length': 102.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.041666666666666664, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.2903675138950348, 'kl': 0.0460205078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 5.44} 68%|██████▊ | 821/1208 [10:38:44<5:33:07, 51.65s/it]Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1697985: cache has only 0 modules Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1698858: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'exchange target and source city'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1699731: cache has only 0 modules [Step 821] loss_orig = 0.001690, loss_refine = -0.281014[Step 821] loss_orig = 0.001999, loss_refine = -1.408342[Step 821] loss_orig = 0.000894, loss_refine = -0.280135 [Step 821] loss_orig = 0.000511, loss_refine = 0.847480[Step 821] loss_orig = 0.000660, loss_refine = 0.849050 [Step 821] loss_orig = 0.000886, loss_refine = 0.849334 [Step 821] loss_orig = 0.000825, loss_refine = -1.408096 [Step 821] loss_orig = 0.001208, loss_refine = 0.848412 68%|██████▊ | 822/1208 [10:39:46<5:52:26, 54.78s/it] {'loss': 0.0017, 'grad_norm': 5.191199010455521, 'learning_rate': 3.1953642384105963e-07, 'completion_length': 97.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.29546840985616046, 'kl': 0.0311279296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.44} 68%|██████▊ | 822/1208 [10:39:46<5:52:26, 54.78s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1700604: cache has only 0 modules Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1701477: cache has only 0 modules 68%|██████▊ | 823/1208 [10:40:23<5:18:17, 49.60s/it] {'loss': 0.0012, 'grad_norm': 13.109287449818988, 'learning_rate': 3.1870860927152314e-07, 'completion_length': 92.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.029052734375, 'clip_ratio': 0.0, 'epoch': 5.45} 68%|██████▊ | 823/1208 [10:40:23<5:18:17, 49.60s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1702350: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1703223: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [453, 448]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1704096: cache has only 0 modules [Step 823] loss_orig = -0.351517, loss_refine = 0.002791[Step 823] loss_orig = -0.351675, loss_refine = 0.003476 [Step 823] loss_orig = -0.351725, loss_refine = 0.001948[Step 823] loss_orig = 2.475949, loss_refine = 0.002689 [Step 823] loss_orig = -0.352536, loss_refine = 0.002954 [Step 823] loss_orig = -0.351216, loss_refine = 0.002199 [Step 823] loss_orig = -0.352055, loss_refine = 0.002559 [Step 823] loss_orig = -0.352486, loss_refine = 0.004987 68%|██████▊ | 824/1208 [10:41:32<5:54:18, 55.36s/it] {'loss': 0.0032, 'grad_norm': 11.550270555845103, 'learning_rate': 3.1788079470198677e-07, 'completion_length': 97.20833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0638427734375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.46} 68%|██████▊ | 824/1208 [10:41:32<5:54:18, 55.36s/it]Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1704969: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1705842: cache has only 0 modules 68%|██████▊ | 825/1208 [10:42:13<5:24:51, 50.89s/it] {'loss': 0.0013, 'grad_norm': 6.979951282278675, 'learning_rate': 3.170529801324503e-07, 'completion_length': 106.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03314208984375, 'clip_ratio': 0.0, 'epoch': 5.46} 68%|██████▊ | 825/1208 [10:42:13<5:24:51, 50.89s/it]Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1706715: cache has only 0 modules Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1707588: cache has only 0 modules 68%|██████▊ | 826/1208 [10:42:52<5:02:50, 47.57s/it] {'loss': 0.0011, 'grad_norm': 0.18991087128180245, 'learning_rate': 3.162251655629139e-07, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02630615234375, 'clip_ratio': 0.0, 'epoch': 5.47} 68%|██████▊ | 826/1208 [10:42:52<5:02:50, 47.57s/it]Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1708461: cache has only 0 modules Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1709334: cache has only 0 modules 68%|██████▊ | 827/1208 [10:43:31<4:45:19, 44.93s/it] {'loss': 0.0018, 'grad_norm': 5.458712696678639, 'learning_rate': 3.153973509933774e-07, 'completion_length': 94.9375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.5345224738121033, 'kl': 0.0438232421875, 'clip_ratio': 0.0, 'epoch': 5.48} 68%|██████▊ | 827/1208 [10:43:31<4:45:19, 44.93s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1710207: cache has only 0 modules Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1711080: cache has only 0 modules 69%|██████▊ | 828/1208 [10:44:13<4:38:56, 44.04s/it] {'loss': 0.0016, 'grad_norm': 7.4753686456579524, 'learning_rate': 3.1456953642384105e-07, 'completion_length': 105.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0390625, 'clip_ratio': 0.0, 'epoch': 5.48} 69%|██████▊ | 828/1208 [10:44:13<4:38:56, 44.04s/it]Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1711953: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1712826: cache has only 0 modules 69%|██████▊ | 829/1208 [10:45:00<4:43:28, 44.88s/it] {'loss': 0.0014, 'grad_norm': 4.438506199685491, 'learning_rate': 3.137417218543046e-07, 'completion_length': 94.8125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.5303300619125366, 'kl': 0.03448486328125, 'clip_ratio': 0.0, 'epoch': 5.49} 69%|██████▊ | 829/1208 [10:45:00<4:43:28, 44.88s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1713699: cache has only 0 modules Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1714572: cache has only 0 modules 69%|██████▊ | 830/1208 [10:45:51<4:54:07, 46.69s/it] {'loss': 0.0013, 'grad_norm': 4.052872065570419, 'learning_rate': 3.129139072847682e-07, 'completion_length': 104.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.5345224738121033, 'kl': 0.03302001953125, 'clip_ratio': 0.0, 'epoch': 5.5} 69%|██████▊ | 830/1208 [10:45:51<4:54:07, 46.69s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1715445: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1716318: cache has only 0 modules 69%|██████▉ | 831/1208 [10:46:39<4:55:19, 47.00s/it] {'loss': 0.0017, 'grad_norm': 23.675633226124148, 'learning_rate': 3.120860927152318e-07, 'completion_length': 119.125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.4375, 'reward_std': 0.4172614812850952, 'kl': 0.0426025390625, 'clip_ratio': 0.0, 'epoch': 5.5} 69%|██████▉ | 831/1208 [10:46:39<4:55:19, 47.00s/it]Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1717191: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1718064: cache has only 0 modules [Step 831] loss_orig = 0.003322, loss_refine = 0.725717[Step 831] loss_orig = 0.001790, loss_refine = -1.205830[Step 831] loss_orig = 0.003399, loss_refine = 0.726508 [Step 831] loss_orig = 0.002799, loss_refine = 0.725857 [Step 831] loss_orig = 0.002776, loss_refine = 0.725994 [Step 831] loss_orig = 0.001566, loss_refine = -1.205683 [Step 831] loss_orig = 0.003134, loss_refine = 0.726318[Step 831] loss_orig = 0.001237, loss_refine = -1.205789 Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1718937: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [1007, 964]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1719810: cache has only 0 modules [Step 831] loss_orig = 0.002242, loss_refine = 0.936637[Step 831] loss_orig = 0.001591, loss_refine = 0.936831 [Step 831] loss_orig = 0.002741, loss_refine = -0.933961 [Step 831] loss_orig = 0.001983, loss_refine = -0.934536[Step 831] loss_orig = 0.000994, loss_refine = 0.936283 [Step 831] loss_orig = 0.001573, loss_refine = -0.934249 [Step 831] loss_orig = 0.000715, loss_refine = -0.933984 [Step 831] loss_orig = 0.006110, loss_refine = 0.936928 69%|██████▉ | 832/1208 [10:48:00<5:59:08, 57.31s/it] {'loss': 0.0014, 'grad_norm': 9.90617979429162, 'learning_rate': 3.1125827814569533e-07, 'completion_length': 97.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.21875, 'reward_std': 0.2630179077386856, 'kl': 0.0592041015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 5.51} 69%|██████▉ | 832/1208 [10:48:00<5:59:08, 57.31s/it]Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1720683: cache has only 0 modules Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1721556: cache has only 0 modules 69%|██████▉ | 833/1208 [10:48:31<5:09:24, 49.50s/it] {'loss': 0.0028, 'grad_norm': 9.469314846826007, 'learning_rate': 3.1043046357615895e-07, 'completion_length': 82.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.07080078125, 'clip_ratio': 0.0, 'epoch': 5.52} 69%|██████▉ | 833/1208 [10:48:31<5:09:24, 49.50s/it]Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1722429: cache has only 0 modules Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1723302: cache has only 0 modules 69%|██████▉ | 834/1208 [10:49:13<4:53:40, 47.11s/it] {'loss': 0.0041, 'grad_norm': 5.650717114543074, 'learning_rate': 3.096026490066225e-07, 'completion_length': 98.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.1016845703125, 'clip_ratio': 0.0, 'epoch': 5.52} 69%|██████▉ | 834/1208 [10:49:13<4:53:40, 47.11s/it]Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1724175: cache has only 0 modules Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1725048: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1725921: cache has only 0 modules [Step 834] loss_orig = -0.538816, loss_refine = 0.001178[Step 834] loss_orig = -0.538137, loss_refine = 0.001138 [Step 834] loss_orig = -0.538185, loss_refine = 0.000559 [Step 834] loss_orig = -0.539163, loss_refine = 0.000910 [Step 834] loss_orig = -0.538762, loss_refine = 0.001372 [Step 834] loss_orig = 1.621255, loss_refine = 0.000945 [Step 834] loss_orig = 1.621310, loss_refine = 0.000574 [Step 834] loss_orig = -0.539471, loss_refine = 0.001252 69%|██████▉ | 835/1208 [10:50:22<5:33:43, 53.68s/it] {'loss': 0.0017, 'grad_norm': 9.511600716730774, 'learning_rate': 3.087748344370861e-07, 'completion_length': 106.75, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.4583333333333335, 'reward_std': 0.42645783225695294, 'kl': 0.0462646484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.53} 69%|██████▉ | 835/1208 [10:50:22<5:33:43, 53.68s/it]Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1726794: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1727667: cache has only 0 modules 69%|██████▉ | 836/1208 [10:50:59<5:02:40, 48.82s/it] {'loss': 0.0012, 'grad_norm': 0.3480749727033554, 'learning_rate': 3.0794701986754966e-07, 'completion_length': 88.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03057861328125, 'clip_ratio': 0.0, 'epoch': 5.54} 69%|██████▉ | 836/1208 [10:50:59<5:02:40, 48.82s/it]Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1728540: cache has only 0 modules Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1729413: cache has only 0 modules 69%|██████▉ | 837/1208 [10:51:34<4:36:09, 44.66s/it] {'loss': 0.0018, 'grad_norm': 13.686186527385154, 'learning_rate': 3.0711920529801323e-07, 'completion_length': 77.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.04443359375, 'clip_ratio': 0.0, 'epoch': 5.54} 69%|██████▉ | 837/1208 [10:51:34<4:36:09, 44.66s/it]Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1730286: cache has only 0 modules Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1731159: cache has only 0 modules 69%|██████▉ | 838/1208 [10:52:28<4:52:22, 47.41s/it] {'loss': 0.0022, 'grad_norm': 8.285110470004282, 'learning_rate': 3.062913907284768e-07, 'completion_length': 114.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 0.9375, 'reward': 2.125, 'reward_std': 0.49721167981624603, 'kl': 0.0537109375, 'clip_ratio': 0.0, 'epoch': 5.55} 69%|██████▉ | 838/1208 [10:52:28<4:52:22, 47.41s/it]Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1732032: cache has only 0 modules Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1732905: cache has only 0 modules 69%|██████▉ | 839/1208 [10:53:01<4:25:35, 43.19s/it] {'loss': 0.0035, 'grad_norm': 9.799593186063241, 'learning_rate': 3.0546357615894037e-07, 'completion_length': 81.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.08837890625, 'clip_ratio': 0.0, 'epoch': 5.56} 69%|██████▉ | 839/1208 [10:53:01<4:25:35, 43.19s/it]Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1733778: cache has only 0 modules Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1734651: cache has only 0 modules 70%|██████▉ | 840/1208 [10:53:44<4:23:52, 43.02s/it] {'loss': 0.0012, 'grad_norm': 0.21708722306768072, 'learning_rate': 3.0463576158940394e-07, 'completion_length': 105.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03021240234375, 'clip_ratio': 0.0, 'epoch': 5.56} 70%|██████▉ | 840/1208 [10:53:44<4:23:52, 43.02s/it]Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1735524: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1736397: cache has only 0 modules [Step 840] loss_orig = -0.352013, loss_refine = -0.352104[Step 840] loss_orig = 2.482588, loss_refine = -0.352123 [Step 840] loss_orig = -0.351175, loss_refine = -0.352019 [Step 840] loss_orig = -0.352573, loss_refine = -0.352136 [Step 840] loss_orig = -0.352430, loss_refine = -0.351862 [Step 840] loss_orig = -0.351603, loss_refine = 2.474894 [Step 840] loss_orig = -0.352648, loss_refine = -0.351906 [Step 840] loss_orig = -0.348676, loss_refine = -0.351493 Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1737270: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1738143: cache has only 0 modules [Step 840] loss_orig = 0.001524, loss_refine = 0.001218[Step 840] loss_orig = 0.001130, loss_refine = 0.001076[Step 840] loss_orig = 0.006259, loss_refine = 0.002497 [Step 840] loss_orig = 0.001941, loss_refine = 0.001453[Step 840] loss_orig = 0.012533, loss_refine = 0.001037 [Step 840] loss_orig = 0.001593, loss_refine = 0.001563 [Step 840] loss_orig = 0.001074, loss_refine = 0.000748 [Step 840] loss_orig = 0.001731, loss_refine = 0.001332 70%|██████▉ | 841/1208 [10:54:48<5:01:05, 49.23s/it] {'loss': 0.0014, 'grad_norm': 15.129684456677907, 'learning_rate': 3.0380794701986756e-07, 'completion_length': 81.9375, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.1767766922712326, 'kl': 0.0771484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 5.57} 70%|██████▉ | 841/1208 [10:54:48<5:01:05, 49.23s/it]Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1739016: cache has only 0 modules Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1739889: cache has only 0 modules 70%|██████▉ | 842/1208 [10:55:30<4:46:44, 47.01s/it] {'loss': 0.0019, 'grad_norm': 7.722667787933315, 'learning_rate': 3.029801324503311e-07, 'completion_length': 103.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0487060546875, 'clip_ratio': 0.0, 'epoch': 5.58} 70%|██████▉ | 842/1208 [10:55:30<4:46:44, 47.01s/it]Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1740762: cache has only 0 modules Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1741635: cache has only 0 modules 70%|██████▉ | 843/1208 [10:56:06<4:26:28, 43.80s/it] {'loss': 0.0016, 'grad_norm': 5.813063291203608, 'learning_rate': 3.021523178807947e-07, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0391845703125, 'clip_ratio': 0.0, 'epoch': 5.58} 70%|██████▉ | 843/1208 [10:56:06<4:26:28, 43.80s/it]Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1742508: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1743381: cache has only 0 modules 70%|██████▉ | 844/1208 [10:56:45<4:17:29, 42.44s/it] {'loss': 0.0022, 'grad_norm': 5.0781404317418435, 'learning_rate': 3.013245033112583e-07, 'completion_length': 93.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.054931640625, 'clip_ratio': 0.0, 'epoch': 5.59} 70%|██████▉ | 844/1208 [10:56:45<4:17:29, 42.44s/it]Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1744254: cache has only 0 modules Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1745127: cache has only 0 modules 70%|██████▉ | 845/1208 [10:57:28<4:17:16, 42.52s/it] {'loss': 0.0036, 'grad_norm': 7.28013955285399, 'learning_rate': 3.0049668874172184e-07, 'completion_length': 98.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.0888671875, 'clip_ratio': 0.0, 'epoch': 5.6} 70%|██████▉ | 845/1208 [10:57:28<4:17:16, 42.52s/it]Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1746000: cache has only 0 modules Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1746873: cache has only 0 modules 70%|███████ | 846/1208 [10:58:04<4:04:59, 40.61s/it] {'loss': 0.0017, 'grad_norm': 22.605918396421092, 'learning_rate': 2.996688741721854e-07, 'completion_length': 98.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0435791015625, 'clip_ratio': 0.0, 'epoch': 5.6} 70%|███████ | 846/1208 [10:58:04<4:04:59, 40.61s/it]Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1747746: cache has only 0 modules Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1748619: cache has only 0 modules 70%|███████ | 847/1208 [10:58:38<3:52:46, 38.69s/it] {'loss': 0.0026, 'grad_norm': 215.7799647875307, 'learning_rate': 2.98841059602649e-07, 'completion_length': 81.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0653076171875, 'clip_ratio': 0.0, 'epoch': 5.61} 70%|███████ | 847/1208 [10:58:38<3:52:46, 38.69s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1749492: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1750365: cache has only 0 modules 70%|███████ | 848/1208 [10:59:22<4:01:10, 40.20s/it] {'loss': 0.0022, 'grad_norm': 0.2781329940116601, 'learning_rate': 2.980132450331126e-07, 'completion_length': 101.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0546875, 'clip_ratio': 0.0, 'epoch': 5.62} 70%|███████ | 848/1208 [10:59:22<4:01:10, 40.20s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1751238: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1752111: cache has only 0 modules 70%|███████ | 849/1208 [11:00:00<3:57:07, 39.63s/it] {'loss': 0.0013, 'grad_norm': 0.17742729805824015, 'learning_rate': 2.971854304635761e-07, 'completion_length': 88.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0313720703125, 'clip_ratio': 0.0, 'epoch': 5.62} 70%|███████ | 849/1208 [11:00:00<3:57:07, 39.63s/it]Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1752984: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1753857: cache has only 0 modules 70%|███████ | 850/1208 [11:00:39<3:54:14, 39.26s/it] {'loss': 0.0031, 'grad_norm': 3.756011328760882, 'learning_rate': 2.9635761589403975e-07, 'completion_length': 95.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.078125, 'clip_ratio': 0.0, 'epoch': 5.63} 70%|███████ | 850/1208 [11:00:39<3:54:14, 39.26s/it]Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1754730: cache has only 0 modules Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1755603: cache has only 0 modules 70%|███████ | 851/1208 [11:01:17<3:52:13, 39.03s/it] {'loss': 0.0017, 'grad_norm': 3.674650952982235, 'learning_rate': 2.9552980132450326e-07, 'completion_length': 95.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.043212890625, 'clip_ratio': 0.0, 'epoch': 5.64} 70%|███████ | 851/1208 [11:01:17<3:52:13, 39.03s/it]Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1756476: cache has only 0 modules Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1757349: cache has only 0 modules 71%|███████ | 852/1208 [11:01:50<3:40:18, 37.13s/it] {'loss': 0.0013, 'grad_norm': 3.2556100151108436, 'learning_rate': 2.947019867549669e-07, 'completion_length': 84.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03173828125, 'clip_ratio': 0.0, 'epoch': 5.64} 71%|███████ | 852/1208 [11:01:50<3:40:18, 37.13s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1758222: cache has only 0 modules Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1759095: cache has only 0 modules 71%|███████ | 853/1208 [11:02:27<3:39:36, 37.12s/it] {'loss': 0.0013, 'grad_norm': 0.26763730483248316, 'learning_rate': 2.938741721854304e-07, 'completion_length': 93.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.031494140625, 'clip_ratio': 0.0, 'epoch': 5.65} 71%|███████ | 853/1208 [11:02:27<3:39:36, 37.12s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1759968: cache has only 0 modules Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1760841: cache has only 0 modules 71%|███████ | 854/1208 [11:03:09<3:47:04, 38.49s/it] {'loss': 0.0016, 'grad_norm': 0.3761492518519191, 'learning_rate': 2.9304635761589403e-07, 'completion_length': 87.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0401611328125, 'clip_ratio': 0.0, 'epoch': 5.66} 71%|███████ | 854/1208 [11:03:09<3:47:04, 38.49s/it]Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1761714: cache has only 0 modules Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1762587: cache has only 0 modules 71%|███████ | 855/1208 [11:03:46<3:45:14, 38.28s/it] {'loss': 0.001, 'grad_norm': 0.2001990504250293, 'learning_rate': 2.922185430463576e-07, 'completion_length': 89.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.024658203125, 'clip_ratio': 0.0, 'epoch': 5.66} 71%|███████ | 855/1208 [11:03:46<3:45:14, 38.28s/it]Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1763460: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Tray Input Indicator - Chinese (Simplified, China)'. Your previous answer was [{'action': 'click', 'coordinate': [2287, 1407] }] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1764333: cache has only 0 modules [Step 855] loss_orig = -0.352233, loss_refine = 0.354861 [Step 855] loss_orig = 2.477053, loss_refine = 0.354622[Step 855] loss_orig = -0.351609, loss_refine = -2.473328 [Step 855] loss_orig = -0.348935, loss_refine = 0.354300[Step 855] loss_orig = -0.346991, loss_refine = 0.354356 [Step 855] loss_orig = -0.352067, loss_refine = 0.354168 [Step 855] loss_orig = -0.351886, loss_refine = 0.354481 [Step 855] loss_orig = -0.348611, loss_refine = 0.354630 Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1765206: cache has only 0 modules 71%|███████ | 856/1208 [11:04:54<4:35:43, 47.00s/it] {'loss': 0.0016, 'grad_norm': 5.823258372441576, 'learning_rate': 2.9139072847682117e-07, 'completion_length': 107.04166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.2916666666666665, 'reward_std': 0.3535533845424652, 'kl': 0.06591796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 5.67} 71%|███████ | 856/1208 [11:04:54<4:35:43, 47.00s/it]Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1766079: cache has only 0 modules Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1766952: cache has only 0 modules 71%|███████ | 857/1208 [11:05:34<4:22:47, 44.92s/it] {'loss': 0.0013, 'grad_norm': 0.235905412780949, 'learning_rate': 2.905629139072848e-07, 'completion_length': 99.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0322265625, 'clip_ratio': 0.0, 'epoch': 5.68} 71%|███████ | 857/1208 [11:05:34<4:22:47, 44.92s/it]Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1767825: cache has only 0 modules Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1768698: cache has only 0 modules 71%|███████ | 858/1208 [11:06:11<4:08:46, 42.65s/it] {'loss': 0.0022, 'grad_norm': 4.15687322029442, 'learning_rate': 2.897350993377483e-07, 'completion_length': 99.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.055908203125, 'clip_ratio': 0.0, 'epoch': 5.68} 71%|███████ | 858/1208 [11:06:11<4:08:46, 42.65s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1769571: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1770444: cache has only 0 modules 71%|███████ | 859/1208 [11:06:48<3:58:07, 40.94s/it] {'loss': 0.0039, 'grad_norm': 19.440815555067953, 'learning_rate': 2.8890728476821193e-07, 'completion_length': 78.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.098388671875, 'clip_ratio': 0.0, 'epoch': 5.69} 71%|███████ | 859/1208 [11:06:48<3:58:07, 40.94s/it]Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1771317: cache has only 0 modules Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1772190: cache has only 0 modules 71%|███████ | 860/1208 [11:07:24<3:48:06, 39.33s/it] {'loss': 0.0012, 'grad_norm': 6.474515694983098, 'learning_rate': 2.8807947019867545e-07, 'completion_length': 88.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02972412109375, 'clip_ratio': 0.0, 'epoch': 5.7} 71%|███████ | 860/1208 [11:07:24<3:48:06, 39.33s/it]Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1773063: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1773936: cache has only 0 modules [Step 860] loss_orig = 0.003319, loss_refine = -1.203100[Step 860] loss_orig = 0.001809, loss_refine = -1.205727[Step 860] loss_orig = 0.003955, loss_refine = 0.725658 [Step 860] loss_orig = 0.002467, loss_refine = 0.727085 [Step 860] loss_orig = 0.000792, loss_refine = 0.725848 [Step 860] loss_orig = 0.001459, loss_refine = 0.725631 [Step 860] loss_orig = 0.001072, loss_refine = 0.726425 [Step 860] loss_orig = 0.001724, loss_refine = -1.205439 Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1774809: cache has only 0 modules 71%|███████▏ | 861/1208 [11:08:11<4:00:36, 41.60s/it] {'loss': 0.0015, 'grad_norm': 4.482210684785932, 'learning_rate': 2.8725165562913907e-07, 'completion_length': 87.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.7} 71%|███████▏ | 861/1208 [11:08:11<4:00:36, 41.60s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1775682: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.625 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1776555: cache has only 0 modules 71%|███████▏ | 862/1208 [11:09:06<4:23:36, 45.71s/it] {'loss': 0.0024, 'grad_norm': 7.844471399117417, 'learning_rate': 2.8642384105960264e-07, 'completion_length': 136.3125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.8125, 'reward': 2.125, 'reward_std': 0.6943650841712952, 'kl': 0.0609130859375, 'clip_ratio': 0.0, 'epoch': 5.71} 71%|███████▏ | 862/1208 [11:09:06<4:23:36, 45.71s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1777428: cache has only 0 modules Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1778301: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'view the outdoor cycle report'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1779174: cache has only 0 modules [Step 862] loss_orig = 0.000445, loss_refine = 0.002428 [Step 862] loss_orig = 0.000647, loss_refine = 0.000511[Step 862] loss_orig = 0.001278, loss_refine = 0.001211 [Step 862] loss_orig = 0.000847, loss_refine = 0.001331 [Step 862] loss_orig = 0.000559, loss_refine = 0.001127 [Step 862] loss_orig = 0.000524, loss_refine = 0.000904 [Step 862] loss_orig = 0.001319, loss_refine = 0.000640 [Step 862] loss_orig = 0.001228, loss_refine = 0.001600 71%|███████▏ | 863/1208 [11:10:02<4:40:17, 48.75s/it] {'loss': 0.0013, 'grad_norm': 0.2901025485936163, 'learning_rate': 2.855960264900662e-07, 'completion_length': 101.95833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6666666666666666, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02728271484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.72} 71%|███████▏ | 863/1208 [11:10:02<4:40:17, 48.75s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1780047: cache has only 0 modules Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1780920: cache has only 0 modules 72%|███████▏ | 864/1208 [11:10:34<4:11:44, 43.91s/it] {'loss': 0.0021, 'grad_norm': 17.78697983747863, 'learning_rate': 2.847682119205298e-07, 'completion_length': 75.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.5260358154773712, 'kl': 0.052734375, 'clip_ratio': 0.0, 'epoch': 5.72} 72%|███████▏ | 864/1208 [11:10:34<4:11:44, 43.91s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1781793: cache has only 0 modules Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1782666: cache has only 0 modules 72%|███████▏ | 865/1208 [11:11:10<3:57:26, 41.53s/it] {'loss': 0.0008, 'grad_norm': 0.1993485662848181, 'learning_rate': 2.8394039735099335e-07, 'completion_length': 94.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0205078125, 'clip_ratio': 0.0, 'epoch': 5.73} 72%|███████▏ | 865/1208 [11:11:10<3:57:26, 41.53s/it]Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1783539: cache has only 0 modules Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1784412: cache has only 0 modules 72%|███████▏ | 866/1208 [11:11:51<3:54:58, 41.22s/it] {'loss': 0.0027, 'grad_norm': 8.599554475519948, 'learning_rate': 2.831125827814569e-07, 'completion_length': 100.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.0677490234375, 'clip_ratio': 0.0, 'epoch': 5.74} 72%|███████▏ | 866/1208 [11:11:51<3:54:58, 41.22s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1785285: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1786158: cache has only 0 modules 72%|███████▏ | 867/1208 [11:12:26<3:44:36, 39.52s/it] {'loss': 0.0021, 'grad_norm': 4.217657779465907, 'learning_rate': 2.8228476821192054e-07, 'completion_length': 80.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0531005859375, 'clip_ratio': 0.0, 'epoch': 5.74} 72%|███████▏ | 867/1208 [11:12:26<3:44:36, 39.52s/it]Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1787031: cache has only 0 modules Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1787904: cache has only 0 modules 72%|███████▏ | 868/1208 [11:13:06<3:44:18, 39.58s/it] {'loss': 0.0021, 'grad_norm': 7.609703134423257, 'learning_rate': 2.8145695364238406e-07, 'completion_length': 89.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.05322265625, 'clip_ratio': 0.0, 'epoch': 5.75} 72%|███████▏ | 868/1208 [11:13:06<3:44:18, 39.58s/it]Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1788777: cache has only 0 modules Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1789650: cache has only 0 modules 72%|███████▏ | 869/1208 [11:13:50<3:51:47, 41.03s/it] {'loss': 0.0012, 'grad_norm': 4.599236140812915, 'learning_rate': 2.806291390728477e-07, 'completion_length': 100.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03125, 'clip_ratio': 0.0, 'epoch': 5.75} 72%|███████▏ | 869/1208 [11:13:50<3:51:47, 41.03s/it]Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1790523: cache has only 0 modules Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1791396: cache has only 0 modules 72%|███████▏ | 870/1208 [11:14:32<3:52:19, 41.24s/it] {'loss': 0.0018, 'grad_norm': 6.689632077454139, 'learning_rate': 2.7980132450331125e-07, 'completion_length': 95.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0460205078125, 'clip_ratio': 0.0, 'epoch': 5.76} 72%|███████▏ | 870/1208 [11:14:32<3:52:19, 41.24s/it]Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1792269: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1793142: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [419, 119]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1794015: cache has only 0 modules [Step 870] loss_orig = 0.001581, loss_refine = -0.723608[Step 870] loss_orig = 0.001496, loss_refine = -0.723027[Step 870] loss_orig = 0.001820, loss_refine = -0.723188[Step 870] loss_orig = 0.001270, loss_refine = -0.723058 [Step 870] loss_orig = 0.001257, loss_refine = 1.210157[Step 870] loss_orig = 0.000899, loss_refine = 1.208850[Step 870] loss_orig = 0.001701, loss_refine = 1.210011 [Step 870] loss_orig = 0.001279, loss_refine = -0.722318 72%|███████▏ | 871/1208 [11:15:22<4:05:50, 43.77s/it] {'loss': 0.0015, 'grad_norm': 10.137938326462852, 'learning_rate': 2.789735099337748e-07, 'completion_length': 95.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0345458984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 5.77} 72%|███████▏ | 871/1208 [11:15:22<4:05:50, 43.77s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1794888: cache has only 0 modules Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1795761: cache has only 0 modules 72%|███████▏ | 872/1208 [11:16:03<3:59:56, 42.85s/it] {'loss': 0.0019, 'grad_norm': 5.364985983855202, 'learning_rate': 2.781456953642384e-07, 'completion_length': 96.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0472412109375, 'clip_ratio': 0.0, 'epoch': 5.77} 72%|███████▏ | 872/1208 [11:16:03<3:59:56, 42.85s/it]Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1796634: cache has only 0 modules Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1797507: cache has only 0 modules 72%|███████▏ | 873/1208 [11:16:46<4:00:21, 43.05s/it] {'loss': 0.0016, 'grad_norm': 13.981288474552109, 'learning_rate': 2.7731788079470196e-07, 'completion_length': 112.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4355512708425522, 'kl': 0.0390625, 'clip_ratio': 0.0, 'epoch': 5.78} 72%|███████▏ | 873/1208 [11:16:46<4:00:21, 43.05s/it]Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1798380: cache has only 0 modules Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1799253: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1800126: cache has only 0 modules [Step 873] loss_orig = 0.003437, loss_refine = 0.727380[Step 873] loss_orig = 0.002506, loss_refine = -1.203454[Step 873] loss_orig = 0.001319, loss_refine = 0.728007[Step 873] loss_orig = 0.001985, loss_refine = 0.726530 [Step 873] loss_orig = 0.002374, loss_refine = 0.727365 [Step 873] loss_orig = 0.001608, loss_refine = -1.203347 [Step 873] loss_orig = 0.001462, loss_refine = 0.726429[Step 873] loss_orig = 0.001500, loss_refine = -1.205864 72%|███████▏ | 874/1208 [11:18:00<4:50:52, 52.25s/it] {'loss': 0.0032, 'grad_norm': 161.48914135970293, 'learning_rate': 2.764900662251656e-07, 'completion_length': 114.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.3506905436515808, 'kl': 0.0701904296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 5.79} 72%|███████▏ | 874/1208 [11:18:00<4:50:52, 52.25s/it]Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1800999: cache has only 0 modules Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1801872: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display ip address'. Your previous answer was [{'action': 'click', 'coordinate': [827, 1090]})] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1802745: cache has only 0 modules [Step 874] loss_orig = 0.002526, loss_refine = -0.934432 [Step 874] loss_orig = 0.001900, loss_refine = -0.934249[Step 874] loss_orig = 0.001469, loss_refine = -0.931250 [Step 874] loss_orig = 0.002848, loss_refine = 0.937918 [Step 874] loss_orig = 0.002112, loss_refine = 0.939635 [Step 874] loss_orig = 0.033758, loss_refine = 0.936583[Step 874] loss_orig = 0.002627, loss_refine = -0.930979 [Step 874] loss_orig = 0.005169, loss_refine = 0.936162 72%|███████▏ | 875/1208 [11:18:42<4:33:31, 49.28s/it] {'loss': 0.0016, 'grad_norm': 4.423894810640087, 'learning_rate': 2.756622516556291e-07, 'completion_length': 79.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.09112548828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 5.79} 72%|███████▏ | 875/1208 [11:18:42<4:33:31, 49.28s/it]Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1803618: cache has only 0 modules Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1804491: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. Your previous answer was [{'action': 'click', 'coordinate': [1119, 110]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1805364: cache has only 0 modules [Step 875] loss_orig = -0.350271, loss_refine = 0.541759[Step 875] loss_orig = -0.351679, loss_refine = 0.543085[Step 875] loss_orig = -0.349004, loss_refine = 0.542818 [Step 875] loss_orig = -0.351889, loss_refine = 0.540949 [Step 875] loss_orig = 2.475984, loss_refine = -1.618229[Step 875] loss_orig = -0.351384, loss_refine = -1.618948 [Step 875] loss_orig = -0.351421, loss_refine = 0.541122 [Step 875] loss_orig = -0.337089, loss_refine = 0.544181 73%|███████▎ | 876/1208 [11:19:45<4:55:07, 53.34s/it] {'loss': 0.0017, 'grad_norm': 5.94992731757987, 'learning_rate': 2.7483443708609273e-07, 'completion_length': 99.16666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.39000560839970905, 'kl': 0.0677490234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 5.8} 73%|███████▎ | 876/1208 [11:19:45<4:55:07, 53.34s/it]Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1806237: cache has only 0 modules Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1807110: cache has only 0 modules 73%|███████▎ | 877/1208 [11:20:22<4:27:10, 48.43s/it] {'loss': 0.0018, 'grad_norm': 4.443643315691267, 'learning_rate': 2.7400662251655625e-07, 'completion_length': 101.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'epoch': 5.81} 73%|███████▎ | 877/1208 [11:20:22<4:27:10, 48.43s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1807983: cache has only 0 modules Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1808856: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open dynamic shot'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1809729: cache has only 0 modules [Step 877] loss_orig = 0.000824, loss_refine = 0.726859[Step 877] loss_orig = 0.001327, loss_refine = -1.205268 [Step 877] loss_orig = 0.001883, loss_refine = 0.726851 [Step 877] loss_orig = 0.001807, loss_refine = 0.726024 [Step 877] loss_orig = 0.001270, loss_refine = 0.727201 [Step 877] loss_orig = 0.000883, loss_refine = -1.205347[Step 877] loss_orig = 0.001415, loss_refine = 0.727199 [Step 877] loss_orig = 0.001612, loss_refine = -1.205625 73%|███████▎ | 878/1208 [11:21:20<4:42:24, 51.35s/it] {'loss': 0.0015, 'grad_norm': 6.584409741875275, 'learning_rate': 2.7317880794701987e-07, 'completion_length': 98.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.3450327714284261, 'kl': 0.0272216796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 5.81} 73%|███████▎ | 878/1208 [11:21:20<4:42:24, 51.35s/it]Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1810602: cache has only 0 modules Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1811475: cache has only 0 modules 73%|███████▎ | 879/1208 [11:21:57<4:17:38, 46.99s/it] {'loss': 0.0013, 'grad_norm': 0.4633781090472948, 'learning_rate': 2.723509933774834e-07, 'completion_length': 85.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0330810546875, 'clip_ratio': 0.0, 'epoch': 5.82} 73%|███████▎ | 879/1208 [11:21:57<4:17:38, 46.99s/it]Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1812348: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1813221: cache has only 0 modules 73%|███████▎ | 880/1208 [11:22:35<4:02:24, 44.34s/it] {'loss': 0.002, 'grad_norm': 0.3141442413842442, 'learning_rate': 2.71523178807947e-07, 'completion_length': 93.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0489501953125, 'clip_ratio': 0.0, 'epoch': 5.83} 73%|███████▎ | 880/1208 [11:22:35<4:02:24, 44.34s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1814094: cache has only 0 modules Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1814967: cache has only 0 modules 73%|███████▎ | 881/1208 [11:23:11<3:48:12, 41.87s/it] {'loss': 0.0012, 'grad_norm': 0.2659873832077331, 'learning_rate': 2.7069536423841063e-07, 'completion_length': 90.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.030517578125, 'clip_ratio': 0.0, 'epoch': 5.83} 73%|███████▎ | 881/1208 [11:23:11<3:48:12, 41.87s/it]Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1815840: cache has only 0 modules Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1816713: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1368, 435]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1817586: cache has only 0 modules [Step 881] loss_orig = 0.001761, loss_refine = 0.354569 [Step 881] loss_orig = 0.001538, loss_refine = -0.586312 [Step 881] loss_orig = 0.003040, loss_refine = -0.586368 [Step 881] loss_orig = 0.000824, loss_refine = -0.587844 [Step 881] loss_orig = 0.000792, loss_refine = 0.354933 [Step 881] loss_orig = 0.001490, loss_refine = -0.587914 [Step 881] loss_orig = 0.002046, loss_refine = 2.239948 [Step 881] loss_orig = 0.001555, loss_refine = -0.587237 73%|███████▎ | 882/1208 [11:24:21<4:33:48, 50.39s/it] {'loss': 0.0013, 'grad_norm': 5.8352977472439855, 'learning_rate': 2.6986754966887415e-07, 'completion_length': 107.33333333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4583333333333335, 'reward_std': 0.35355337460835773, 'kl': 0.03228759765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 5.84} 73%|███████▎ | 882/1208 [11:24:21<4:33:48, 50.39s/it]Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1818459: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1819332: cache has only 0 modules [Step 882] loss_orig = 0.001010, loss_refine = -0.351294[Step 882] loss_orig = 0.000505, loss_refine = -0.351408 [Step 882] loss_orig = 0.000515, loss_refine = 2.479134[Step 882] loss_orig = 0.000938, loss_refine = -0.349707[Step 882] loss_orig = 0.001539, loss_refine = -0.351000 [Step 882] loss_orig = 0.000849, loss_refine = -0.351897 [Step 882] loss_orig = 0.000970, loss_refine = -0.351296 [Step 882] loss_orig = 0.005306, loss_refine = -0.350063 Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1820205: cache has only 0 modules 73%|███████▎ | 883/1208 [11:25:06<4:23:27, 48.64s/it] {'loss': 0.002, 'grad_norm': 26.157058657635925, 'learning_rate': 2.6903973509933777e-07, 'completion_length': 87.20833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.41387641429901123, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 5.85} 73%|███████▎ | 883/1208 [11:25:06<4:23:27, 48.64s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1821078: cache has only 0 modules Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1821951: cache has only 0 modules 73%|███████▎ | 884/1208 [11:25:46<4:07:52, 45.90s/it] {'loss': 0.0023, 'grad_norm': 7.055865881286689, 'learning_rate': 2.682119205298013e-07, 'completion_length': 96.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.0576171875, 'clip_ratio': 0.0, 'epoch': 5.85} 73%|███████▎ | 884/1208 [11:25:46<4:07:52, 45.90s/it]Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1822824: cache has only 0 modules Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1823697: cache has only 0 modules 73%|███████▎ | 885/1208 [11:26:28<4:01:41, 44.90s/it] {'loss': 0.0011, 'grad_norm': 0.2802456797657103, 'learning_rate': 2.673841059602649e-07, 'completion_length': 104.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.027984619140625, 'clip_ratio': 0.0, 'epoch': 5.86} 73%|███████▎ | 885/1208 [11:26:28<4:01:41, 44.90s/it]Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1824570: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1825443: cache has only 0 modules 73%|███████▎ | 886/1208 [11:27:02<3:43:20, 41.62s/it] {'loss': 0.0013, 'grad_norm': 0.4955527032568618, 'learning_rate': 2.6655629139072843e-07, 'completion_length': 83.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03326416015625, 'clip_ratio': 0.0, 'epoch': 5.87} 73%|███████▎ | 886/1208 [11:27:02<3:43:20, 41.62s/it]Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1826316: cache has only 0 modules Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1827189: cache has only 0 modules 73%|███████▎ | 887/1208 [11:27:43<3:40:57, 41.30s/it] {'loss': 0.0015, 'grad_norm': 28.81650324058654, 'learning_rate': 2.6572847682119205e-07, 'completion_length': 97.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.7112991660833359, 'kl': 0.03851318359375, 'clip_ratio': 0.0, 'epoch': 5.87} 73%|███████▎ | 887/1208 [11:27:43<3:40:57, 41.30s/it]Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1828062: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1828935: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [600, 1429]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1829808: cache has only 0 modules [Step 887] loss_orig = 0.004251, loss_refine = 1.209786[Step 887] loss_orig = 0.002679, loss_refine = -0.723409[Step 887] loss_orig = 0.001675, loss_refine = 1.208691 [Step 887] loss_orig = 0.001757, loss_refine = -0.723030 [Step 887] loss_orig = 0.002788, loss_refine = -0.721402[Step 887] loss_orig = 0.002061, loss_refine = 1.209230[Step 887] loss_orig = 0.000508, loss_refine = -0.722920 [Step 887] loss_orig = 0.001087, loss_refine = -0.723170 74%|███████▎ | 888/1208 [11:28:41<4:07:58, 46.49s/it] {'loss': 0.0016, 'grad_norm': 21.537066317359983, 'learning_rate': 2.649006622516556e-07, 'completion_length': 98.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.3450327714284261, 'kl': 0.0439453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 5.88} 74%|███████▎ | 888/1208 [11:28:41<4:07:58, 46.49s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1830681: cache has only 0 modules Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1831554: cache has only 0 modules 74%|███████▎ | 889/1208 [11:29:17<3:49:42, 43.20s/it] {'loss': 0.0023, 'grad_norm': 6.681061462589449, 'learning_rate': 2.640728476821192e-07, 'completion_length': 97.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0579833984375, 'clip_ratio': 0.0, 'epoch': 5.89} 74%|███████▎ | 889/1208 [11:29:17<3:49:42, 43.20s/it]Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1832427: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1833300: cache has only 0 modules [Step 889] loss_orig = 0.002133, loss_refine = -0.537736[Step 889] loss_orig = 0.001239, loss_refine = -0.538134 [Step 889] loss_orig = 0.001748, loss_refine = -0.538969 [Step 889] loss_orig = 0.001207, loss_refine = 1.621358 [Step 889] loss_orig = 0.001692, loss_refine = 1.621112 [Step 889] loss_orig = 0.001254, loss_refine = -0.538025 [Step 889] loss_orig = 0.001062, loss_refine = -0.537672 [Step 889] loss_orig = 0.002273, loss_refine = -0.537508 Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1834173: cache has only 0 modules 74%|███████▎ | 890/1208 [11:30:09<4:03:06, 45.87s/it] {'loss': 0.0014, 'grad_norm': 9.080467375855745, 'learning_rate': 2.6324503311258276e-07, 'completion_length': 92.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.0321044921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 5.89} 74%|███████▎ | 890/1208 [11:30:09<4:03:06, 45.87s/it]Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1835046: cache has only 0 modules Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1835919: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1836792: cache has only 0 modules [Step 890] loss_orig = 0.000634, loss_refine = 0.725754 [Step 890] loss_orig = 0.001473, loss_refine = -1.205173 [Step 890] loss_orig = 0.001186, loss_refine = 0.725933[Step 890] loss_orig = 0.001148, loss_refine = 0.726148 [Step 890] loss_orig = 0.001056, loss_refine = 0.725776[Step 890] loss_orig = 0.000820, loss_refine = -1.205968 [Step 890] loss_orig = 0.001714, loss_refine = -1.205407 [Step 890] loss_orig = 0.001030, loss_refine = 0.725673 74%|███████▍ | 891/1208 [11:31:03<4:15:01, 48.27s/it] {'loss': 0.0015, 'grad_norm': 7.8578550125631805, 'learning_rate': 2.6241721854304633e-07, 'completion_length': 93.54166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0308837890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 5.9} 74%|███████▍ | 891/1208 [11:31:03<4:15:01, 48.27s/it]Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1837665: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1838538: cache has only 0 modules 74%|███████▍ | 892/1208 [11:31:41<3:58:58, 45.37s/it] {'loss': 0.0021, 'grad_norm': 7.952612880874003, 'learning_rate': 2.615894039735099e-07, 'completion_length': 85.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0533447265625, 'clip_ratio': 0.0, 'epoch': 5.91} 74%|███████▍ | 892/1208 [11:31:41<3:58:58, 45.37s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1839411: cache has only 0 modules Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1840284: cache has only 0 modules 74%|███████▍ | 893/1208 [11:32:17<3:43:02, 42.48s/it] {'loss': 0.0009, 'grad_norm': 0.2687790983912377, 'learning_rate': 2.607615894039735e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.022247314453125, 'clip_ratio': 0.0, 'epoch': 5.91} 74%|███████▍ | 893/1208 [11:32:17<3:43:02, 42.48s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1841157: cache has only 0 modules Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1842030: cache has only 0 modules 74%|███████▍ | 894/1208 [11:32:55<3:35:40, 41.21s/it] {'loss': 0.0011, 'grad_norm': 3.798738859342111, 'learning_rate': 2.599337748344371e-07, 'completion_length': 96.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02734375, 'clip_ratio': 0.0, 'epoch': 5.92} 74%|███████▍ | 894/1208 [11:32:55<3:35:40, 41.21s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1842903: cache has only 0 modules Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1843776: cache has only 0 modules 74%|███████▍ | 895/1208 [11:33:36<3:34:35, 41.13s/it] {'loss': 0.001, 'grad_norm': 7.170107704290281, 'learning_rate': 2.5910596026490067e-07, 'completion_length': 99.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.02459716796875, 'clip_ratio': 0.0, 'epoch': 5.93} 74%|███████▍ | 895/1208 [11:33:36<3:34:35, 41.13s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1844649: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1845522: cache has only 0 modules 74%|███████▍ | 896/1208 [11:34:13<3:27:35, 39.92s/it] {'loss': 0.0017, 'grad_norm': 0.34704491246578095, 'learning_rate': 2.5827814569536424e-07, 'completion_length': 100.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.042236328125, 'clip_ratio': 0.0, 'epoch': 5.93} 74%|███████▍ | 896/1208 [11:34:13<3:27:35, 39.92s/it]Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1846395: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1847268: cache has only 0 modules 74%|███████▍ | 897/1208 [11:34:58<3:33:51, 41.26s/it] {'loss': 0.0011, 'grad_norm': 36.11306904679889, 'learning_rate': 2.574503311258278e-07, 'completion_length': 100.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02825927734375, 'clip_ratio': 0.0, 'epoch': 5.94} 74%|███████▍ | 897/1208 [11:34:58<3:33:51, 41.26s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1848141: cache has only 0 modules Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1849014: cache has only 0 modules 74%|███████▍ | 898/1208 [11:35:42<3:38:21, 42.26s/it] {'loss': 0.0017, 'grad_norm': 4.868979861949254, 'learning_rate': 2.566225165562914e-07, 'completion_length': 95.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.041259765625, 'clip_ratio': 0.0, 'epoch': 5.95} 74%|███████▍ | 898/1208 [11:35:42<3:38:21, 42.26s/it]Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1849887: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1850760: cache has only 0 modules 74%|███████▍ | 899/1208 [11:36:22<3:33:03, 41.37s/it] {'loss': 0.0035, 'grad_norm': 0.8245556236164318, 'learning_rate': 2.5579470198675495e-07, 'completion_length': 91.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0887451171875, 'clip_ratio': 0.0, 'epoch': 5.95} 74%|███████▍ | 899/1208 [11:36:22<3:33:03, 41.37s/it]Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1851633: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1852506: cache has only 0 modules 75%|███████▍ | 900/1208 [11:37:01<3:29:32, 40.82s/it] {'loss': 0.0024, 'grad_norm': 3.9738514549179698, 'learning_rate': 2.5496688741721857e-07, 'completion_length': 101.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.06072998046875, 'clip_ratio': 0.0, 'epoch': 5.96} 75%|███████▍ | 900/1208 [11:37:01<3:29:32, 40.82s/it]Start loss calc for inst: click the UI element Zoom 376% /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1853379: cache has only 0 modules Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1854252: cache has only 0 modules 75%|███████▍ | 901/1208 [11:38:11<4:14:05, 49.66s/it] {'loss': 0.0022, 'grad_norm': 4.380171624398679, 'learning_rate': 2.541390728476821e-07, 'completion_length': 108.0, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.5303300619125366, 'kl': 0.055908203125, 'clip_ratio': 0.0, 'epoch': 5.97} 75%|███████▍ | 901/1208 [11:38:11<4:14:05, 49.66s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1855125: cache has only 0 modules Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1855998: cache has only 0 modules 75%|███████▍ | 902/1208 [11:38:48<3:52:59, 45.68s/it] {'loss': 0.001, 'grad_norm': 11.160993197753285, 'learning_rate': 2.533112582781457e-07, 'completion_length': 96.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.02496337890625, 'clip_ratio': 0.0, 'epoch': 5.97} 75%|███████▍ | 902/1208 [11:38:48<3:52:59, 45.68s/it]Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1856871: cache has only 0 modules Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1857744: cache has only 0 modules 75%|███████▍ | 903/1208 [11:39:27<3:42:03, 43.68s/it] {'loss': 0.0008, 'grad_norm': 0.2804774018133164, 'learning_rate': 2.524834437086092e-07, 'completion_length': 88.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.020751953125, 'clip_ratio': 0.0, 'epoch': 5.98} 75%|███████▍ | 903/1208 [11:39:27<3:42:03, 43.68s/it]Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1858617: cache has only 0 modules Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1859490: cache has only 0 modules 75%|███████▍ | 904/1208 [11:40:10<3:40:52, 43.59s/it] {'loss': 0.0011, 'grad_norm': 3.1825603785317904, 'learning_rate': 2.5165562913907285e-07, 'completion_length': 110.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.027099609375, 'clip_ratio': 0.0, 'epoch': 5.99} 75%|███████▍ | 904/1208 [11:40:10<3:40:52, 43.59s/it]Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1860363: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1861236: cache has only 0 modules 75%|███████▍ | 905/1208 [11:40:44<3:25:11, 40.63s/it] {'loss': 0.001, 'grad_norm': 15.6464436986449, 'learning_rate': 2.5082781456953637e-07, 'completion_length': 93.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.026123046875, 'clip_ratio': 0.0, 'epoch': 5.99} 75%|███████▍ | 905/1208 [11:40:44<3:25:11, 40.63s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1862109: cache has only 0 modules Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.8333333730697632 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1862982: cache has only 0 modules 75%|███████▌ | 906/1208 [11:41:23<3:22:10, 40.17s/it] {'loss': 0.0018, 'grad_norm': 4.5164434833632345, 'learning_rate': 2.5e-07, 'completion_length': 91.25000381469727, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9166666865348816, 'rewards/format_reward': 1.0, 'reward': 2.9166667461395264, 'reward_std': 0.1767766922712326, 'kl': 0.0450439453125, 'clip_ratio': 0.0, 'epoch': 6.0} 75%|███████▌ | 906/1208 [11:41:23<3:22:10, 40.17s/it]Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1863855: cache has only 0 modules Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1864728: cache has only 0 modules 75%|███████▌ | 907/1208 [11:42:04<3:22:23, 40.35s/it] {'loss': 0.001, 'grad_norm': 4.487421546205019, 'learning_rate': 2.4917218543046356e-07, 'completion_length': 93.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.024658203125, 'clip_ratio': 0.0, 'epoch': 6.01} 75%|███████▌ | 907/1208 [11:42:04<3:22:23, 40.35s/it]Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1865601: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1866474: cache has only 0 modules [Step 907] loss_orig = 0.002155, loss_refine = 0.355429 [Step 907] loss_orig = 0.000965, loss_refine = 0.357198[Step 907] loss_orig = 0.002573, loss_refine = 0.354670 [Step 907] loss_orig = 0.001997, loss_refine = 0.355477[Step 907] loss_orig = 0.001470, loss_refine = 0.359634 [Step 907] loss_orig = 0.002510, loss_refine = 0.354466 [Step 907] loss_orig = 0.002007, loss_refine = 0.355545 [Step 907] loss_orig = 0.003456, loss_refine = -2.471875 Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1867347: cache has only 0 modules 75%|███████▌ | 908/1208 [11:43:08<3:56:55, 47.38s/it] {'loss': 0.0026, 'grad_norm': 9.371170205483375, 'learning_rate': 2.4834437086092713e-07, 'completion_length': 102.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.2903675138950348, 'kl': 0.0592041015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.01} 75%|███████▌ | 908/1208 [11:43:08<3:56:55, 47.38s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1868220: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1869093: cache has only 0 modules 75%|███████▌ | 909/1208 [11:43:54<3:55:00, 47.16s/it] {'loss': 0.0062, 'grad_norm': 23.62158996031274, 'learning_rate': 2.475165562913907e-07, 'completion_length': 101.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.4355512708425522, 'kl': 0.15509033203125, 'clip_ratio': 0.0, 'epoch': 6.02} 75%|███████▌ | 909/1208 [11:43:54<3:55:00, 47.16s/it]Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1869966: cache has only 0 modules Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1870839: cache has only 0 modules 75%|███████▌ | 910/1208 [11:44:35<3:45:06, 45.32s/it] {'loss': 0.0013, 'grad_norm': 37.79606385795987, 'learning_rate': 2.466887417218543e-07, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0316162109375, 'clip_ratio': 0.0, 'epoch': 6.03} 75%|███████▌ | 910/1208 [11:44:35<3:45:06, 45.32s/it]Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1871712: cache has only 0 modules Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1872585: cache has only 0 modules 75%|███████▌ | 911/1208 [11:45:24<3:49:14, 46.31s/it] {'loss': 0.0013, 'grad_norm': 8.754838081001287, 'learning_rate': 2.458609271523179e-07, 'completion_length': 106.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.03143310546875, 'clip_ratio': 0.0, 'epoch': 6.03} 75%|███████▌ | 911/1208 [11:45:24<3:49:14, 46.31s/it]Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1873458: cache has only 0 modules Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1874331: cache has only 0 modules 75%|███████▌ | 912/1208 [11:46:03<3:37:55, 44.17s/it] {'loss': 0.0006, 'grad_norm': 5.869537545412926, 'learning_rate': 2.4503311258278146e-07, 'completion_length': 105.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0155029296875, 'clip_ratio': 0.0, 'epoch': 6.04} 75%|███████▌ | 912/1208 [11:46:03<3:37:55, 44.17s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1875204: cache has only 0 modules Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1876077: cache has only 0 modules 76%|███████▌ | 913/1208 [11:46:46<3:35:29, 43.83s/it] {'loss': 0.0014, 'grad_norm': 12.634376738988593, 'learning_rate': 2.4420529801324503e-07, 'completion_length': 92.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.03582763671875, 'clip_ratio': 0.0, 'epoch': 6.05} 76%|███████▌ | 913/1208 [11:46:46<3:35:29, 43.83s/it]Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1876950: cache has only 0 modules Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1877823: cache has only 0 modules 76%|███████▌ | 914/1208 [11:47:23<3:25:13, 41.88s/it] {'loss': 0.0034, 'grad_norm': 18.446917399405077, 'learning_rate': 2.433774834437086e-07, 'completion_length': 103.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.08544921875, 'clip_ratio': 0.0, 'epoch': 6.05} 76%|███████▌ | 914/1208 [11:47:23<3:25:13, 41.88s/it]Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1878696: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1879569: cache has only 0 modules 76%|███████▌ | 915/1208 [11:48:09<3:30:06, 43.02s/it] {'loss': 0.0015, 'grad_norm': 0.18352151080367732, 'learning_rate': 2.4254966887417217e-07, 'completion_length': 106.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03778076171875, 'clip_ratio': 0.0, 'epoch': 6.06} 76%|███████▌ | 915/1208 [11:48:09<3:30:06, 43.02s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1880442: cache has only 0 modules Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1881315: cache has only 0 modules 76%|███████▌ | 916/1208 [11:48:46<3:20:33, 41.21s/it] {'loss': 0.001, 'grad_norm': 9.514613938049646, 'learning_rate': 2.4172185430463574e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.02490234375, 'clip_ratio': 0.0, 'epoch': 6.07} 76%|███████▌ | 916/1208 [11:48:46<3:20:33, 41.21s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1882188: cache has only 0 modules Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1883061: cache has only 0 modules 76%|███████▌ | 917/1208 [11:49:33<3:28:14, 42.94s/it] {'loss': 0.0015, 'grad_norm': 7.799578461645501, 'learning_rate': 2.408940397350993e-07, 'completion_length': 125.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03759765625, 'clip_ratio': 0.0, 'epoch': 6.07} 76%|███████▌ | 917/1208 [11:49:33<3:28:14, 42.94s/it]Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1883934: cache has only 0 modules Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1884807: cache has only 0 modules 76%|███████▌ | 918/1208 [11:50:13<3:22:39, 41.93s/it] {'loss': 0.0014, 'grad_norm': 8.299288149271666, 'learning_rate': 2.400662251655629e-07, 'completion_length': 96.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.0360107421875, 'clip_ratio': 0.0, 'epoch': 6.08} 76%|███████▌ | 918/1208 [11:50:13<3:22:39, 41.93s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1885680: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1886553: cache has only 0 modules [Step 918] loss_orig = 0.001075, loss_refine = 0.937231[Step 918] loss_orig = 0.001198, loss_refine = -0.933722 [Step 918] loss_orig = 0.000653, loss_refine = 0.936380[Step 918] loss_orig = 0.001501, loss_refine = -0.934000[Step 918] loss_orig = 0.001098, loss_refine = -0.927089 [Step 918] loss_orig = 0.001501, loss_refine = 0.936823[Step 918] loss_orig = 0.001874, loss_refine = -0.933553 [Step 918] loss_orig = 0.001709, loss_refine = 0.936680 Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1887426: cache has only 0 modules 76%|███████▌ | 919/1208 [11:51:14<3:49:43, 47.69s/it] {'loss': 0.0015, 'grad_norm': 11.278176381703318, 'learning_rate': 2.392384105960265e-07, 'completion_length': 104.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.17817415793736777, 'kl': 0.02587890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 6.09} 76%|███████▌ | 919/1208 [11:51:14<3:49:43, 47.69s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1888299: cache has only 0 modules Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1889172: cache has only 0 modules 76%|███████▌ | 920/1208 [11:51:50<3:31:56, 44.16s/it] {'loss': 0.001, 'grad_norm': 3.6329995718693175, 'learning_rate': 2.3841059602649005e-07, 'completion_length': 81.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.025390625, 'clip_ratio': 0.0, 'epoch': 6.09} 76%|███████▌ | 920/1208 [11:51:50<3:31:56, 44.16s/it]Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1890045: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1890918: cache has only 0 modules 76%|███████▌ | 921/1208 [11:52:28<3:23:07, 42.47s/it] {'loss': 0.0012, 'grad_norm': 0.19823926344624643, 'learning_rate': 2.3758278145695362e-07, 'completion_length': 98.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0302734375, 'clip_ratio': 0.0, 'epoch': 6.1} 76%|███████▌ | 921/1208 [11:52:28<3:23:07, 42.47s/it]Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1891791: cache has only 0 modules Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1892664: cache has only 0 modules 76%|███████▋ | 922/1208 [11:53:09<3:20:15, 42.01s/it] {'loss': 0.002, 'grad_norm': 11.792782315115968, 'learning_rate': 2.367549668874172e-07, 'completion_length': 98.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.05072021484375, 'clip_ratio': 0.0, 'epoch': 6.11} 76%|███████▋ | 922/1208 [11:53:09<3:20:15, 42.01s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1893537: cache has only 0 modules Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1894410: cache has only 0 modules 76%|███████▋ | 923/1208 [11:53:53<3:21:34, 42.44s/it] {'loss': 0.001, 'grad_norm': 0.15748138889651597, 'learning_rate': 2.3592715231788079e-07, 'completion_length': 89.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02520751953125, 'clip_ratio': 0.0, 'epoch': 6.11} 76%|███████▋ | 923/1208 [11:53:53<3:21:34, 42.44s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1895283: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1896156: cache has only 0 modules 76%|███████▋ | 924/1208 [11:54:35<3:20:34, 42.37s/it] {'loss': 0.0029, 'grad_norm': 19.672956175702204, 'learning_rate': 2.3509933774834436e-07, 'completion_length': 105.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.07208251953125, 'clip_ratio': 0.0, 'epoch': 6.12} 76%|███████▋ | 924/1208 [11:54:35<3:20:34, 42.37s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1897029: cache has only 0 modules Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1897902: cache has only 0 modules 77%|███████▋ | 925/1208 [11:55:12<3:13:03, 40.93s/it] {'loss': 0.0019, 'grad_norm': 0.606870293431394, 'learning_rate': 2.3427152317880795e-07, 'completion_length': 98.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0474853515625, 'clip_ratio': 0.0, 'epoch': 6.13} 77%|███████▋ | 925/1208 [11:55:12<3:13:03, 40.93s/it]Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1898775: cache has only 0 modules Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1899648: cache has only 0 modules 77%|███████▋ | 926/1208 [11:55:50<3:07:22, 39.87s/it] {'loss': 0.0012, 'grad_norm': 9.688278224374818, 'learning_rate': 2.3344370860927152e-07, 'completion_length': 91.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.02972412109375, 'clip_ratio': 0.0, 'epoch': 6.13} 77%|███████▋ | 926/1208 [11:55:50<3:07:22, 39.87s/it]Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1900521: cache has only 0 modules Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1901394: cache has only 0 modules 77%|███████▋ | 927/1208 [11:56:27<3:02:58, 39.07s/it] {'loss': 0.0024, 'grad_norm': 0.2705611360019845, 'learning_rate': 2.326158940397351e-07, 'completion_length': 81.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.05926513671875, 'clip_ratio': 0.0, 'epoch': 6.14} 77%|███████▋ | 927/1208 [11:56:27<3:02:58, 39.07s/it]Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1902267: cache has only 0 modules Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1903140: cache has only 0 modules 77%|███████▋ | 928/1208 [11:57:07<3:03:40, 39.36s/it] {'loss': 0.0016, 'grad_norm': 61.15035585974205, 'learning_rate': 2.3178807947019866e-07, 'completion_length': 99.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.039306640625, 'clip_ratio': 0.0, 'epoch': 6.15} 77%|███████▋ | 928/1208 [11:57:07<3:03:40, 39.36s/it]Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1904013: cache has only 0 modules Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1904886: cache has only 0 modules 77%|███████▋ | 929/1208 [11:57:45<3:01:40, 39.07s/it] {'loss': 0.0012, 'grad_norm': 0.6493704758458745, 'learning_rate': 2.3096026490066226e-07, 'completion_length': 88.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0294189453125, 'clip_ratio': 0.0, 'epoch': 6.15} 77%|███████▋ | 929/1208 [11:57:45<3:01:40, 39.07s/it]Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1905759: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1906632: cache has only 0 modules 77%|███████▋ | 930/1208 [11:58:23<2:58:57, 38.62s/it] {'loss': 0.0011, 'grad_norm': 3.559162349477439, 'learning_rate': 2.3013245033112583e-07, 'completion_length': 95.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.02655029296875, 'clip_ratio': 0.0, 'epoch': 6.16} 77%|███████▋ | 930/1208 [11:58:23<2:58:57, 38.62s/it]Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1907505: cache has only 0 modules Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1908378: cache has only 0 modules 77%|███████▋ | 931/1208 [11:59:09<3:08:48, 40.90s/it] {'loss': 0.0014, 'grad_norm': 0.32258569475976945, 'learning_rate': 2.293046357615894e-07, 'completion_length': 91.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0355224609375, 'clip_ratio': 0.0, 'epoch': 6.17} 77%|███████▋ | 931/1208 [11:59:09<3:08:48, 40.90s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1909251: cache has only 0 modules Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1910124: cache has only 0 modules 77%|███████▋ | 932/1208 [11:59:48<3:04:59, 40.22s/it] {'loss': 0.0016, 'grad_norm': 4.813901167100808, 'learning_rate': 2.2847682119205297e-07, 'completion_length': 93.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.03887939453125, 'clip_ratio': 0.0, 'epoch': 6.17} 77%|███████▋ | 932/1208 [11:59:48<3:04:59, 40.22s/it]Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1910997: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1911870: cache has only 0 modules [Step 932] loss_orig = 0.002102, loss_refine = -0.352540 [Step 932] loss_orig = 0.002180, loss_refine = -0.352367[Step 932] loss_orig = 0.001426, loss_refine = -0.351458 [Step 932] loss_orig = 0.001395, loss_refine = -0.350805 [Step 932] loss_orig = 0.001102, loss_refine = -0.350687 [Step 932] loss_orig = 0.001411, loss_refine = -0.351909 [Step 932] loss_orig = 0.003048, loss_refine = -0.352575 [Step 932] loss_orig = 0.001583, loss_refine = 2.475518 Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1912743: cache has only 0 modules 77%|███████▋ | 933/1208 [12:01:01<3:49:05, 49.98s/it] {'loss': 0.004, 'grad_norm': 6.436609883863679, 'learning_rate': 2.2764900662251654e-07, 'completion_length': 109.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.1024169921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 6.18} 77%|███████▋ | 933/1208 [12:01:01<3:49:05, 49.98s/it]Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1913616: cache has only 0 modules Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1914489: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1915362: cache has only 0 modules [Step 933] loss_orig = 0.001376, loss_refine = 0.541881[Step 933] loss_orig = 0.001617, loss_refine = 0.541292 [Step 933] loss_orig = 0.003761, loss_refine = 0.540619 [Step 933] loss_orig = 0.001340, loss_refine = -1.618129[Step 933] loss_orig = 0.000967, loss_refine = 0.540473 [Step 933] loss_orig = 0.000868, loss_refine = 0.540862 [Step 933] loss_orig = 0.000783, loss_refine = -1.618083 [Step 933] loss_orig = 0.001043, loss_refine = 0.541749 77%|███████▋ | 934/1208 [12:02:04<4:06:31, 53.98s/it] {'loss': 0.001, 'grad_norm': 7.194795375936273, 'learning_rate': 2.268211920529801e-07, 'completion_length': 106.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.02618408203125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 6.19} 77%|███████▋ | 934/1208 [12:02:04<4:06:31, 53.98s/it]Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1916235: cache has only 0 modules Start loss calc for inst: add a new one Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1917108: cache has only 0 modules 77%|███████▋ | 935/1208 [12:02:43<3:44:42, 49.39s/it] {'loss': 0.0017, 'grad_norm': 40.74115931422092, 'learning_rate': 2.2599337748344368e-07, 'completion_length': 91.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0430908203125, 'clip_ratio': 0.0, 'epoch': 6.19} 77%|███████▋ | 935/1208 [12:02:43<3:44:42, 49.39s/it]Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1917981: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1918854: cache has only 0 modules 77%|███████▋ | 936/1208 [12:03:25<3:34:42, 47.36s/it] {'loss': 0.002, 'grad_norm': 6.249223012788634, 'learning_rate': 2.2516556291390728e-07, 'completion_length': 116.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.5175491571426392, 'kl': 0.05078125, 'clip_ratio': 0.0, 'epoch': 6.2} 77%|███████▋ | 936/1208 [12:03:25<3:34:42, 47.36s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1919727: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1920600: cache has only 0 modules 78%|███████▊ | 937/1208 [12:04:10<3:30:30, 46.61s/it] {'loss': 0.0017, 'grad_norm': 11.817061776385694, 'learning_rate': 2.2433774834437085e-07, 'completion_length': 107.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04345703125, 'clip_ratio': 0.0, 'epoch': 6.21} 78%|███████▊ | 937/1208 [12:04:10<3:30:30, 46.61s/it]Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1921473: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1922346: cache has only 0 modules [Step 937] loss_orig = 0.002749, loss_refine = 0.001501[Step 937] loss_orig = 0.001150, loss_refine = 0.001621 [Step 937] loss_orig = 0.001156, loss_refine = 0.001698[Step 937] loss_orig = 0.001442, loss_refine = 0.001760[Step 937] loss_orig = 0.002542, loss_refine = 0.001671 [Step 937] loss_orig = 0.001278, loss_refine = 0.005800 [Step 937] loss_orig = 0.001959, loss_refine = 0.001364[Step 937] loss_orig = 0.001930, loss_refine = 0.001481 Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1923219: cache has only 0 modules 78%|███████▊ | 938/1208 [12:05:18<3:58:35, 53.02s/it] {'loss': 0.0014, 'grad_norm': 0.5122367708971336, 'learning_rate': 2.2350993377483444e-07, 'completion_length': 99.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.0299072265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.21} 78%|███████▊ | 938/1208 [12:05:18<3:58:35, 53.02s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1924092: cache has only 0 modules Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1924965: cache has only 0 modules 78%|███████▊ | 939/1208 [12:05:58<3:40:34, 49.20s/it] {'loss': 0.0015, 'grad_norm': 9.25477646393217, 'learning_rate': 2.22682119205298e-07, 'completion_length': 95.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.0377197265625, 'clip_ratio': 0.0, 'epoch': 6.22} 78%|███████▊ | 939/1208 [12:05:58<3:40:34, 49.20s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1925838: cache has only 0 modules Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1926711: cache has only 0 modules 78%|███████▊ | 940/1208 [12:06:39<3:28:04, 46.58s/it] {'loss': 0.0015, 'grad_norm': 4.63615145761505, 'learning_rate': 2.2185430463576158e-07, 'completion_length': 97.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.0377197265625, 'clip_ratio': 0.0, 'epoch': 6.23} 78%|███████▊ | 940/1208 [12:06:39<3:28:04, 46.58s/it]Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1927584: cache has only 0 modules Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1928457: cache has only 0 modules 78%|███████▊ | 941/1208 [12:07:18<3:16:58, 44.27s/it] {'loss': 0.0014, 'grad_norm': 6.098552482737498, 'learning_rate': 2.2102649006622515e-07, 'completion_length': 90.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.03485107421875, 'clip_ratio': 0.0, 'epoch': 6.23} 78%|███████▊ | 941/1208 [12:07:18<3:16:58, 44.27s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1929330: cache has only 0 modules Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1930203: cache has only 0 modules 78%|███████▊ | 942/1208 [12:07:57<3:09:19, 42.71s/it] {'loss': 0.0019, 'grad_norm': 5.154705993797319, 'learning_rate': 2.2019867549668872e-07, 'completion_length': 82.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.047607421875, 'clip_ratio': 0.0, 'epoch': 6.24} 78%|███████▊ | 942/1208 [12:07:57<3:09:19, 42.71s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1931076: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1931949: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1932822: cache has only 0 modules [Step 942] loss_orig = 0.003648, loss_refine = 0.001830[Step 942] loss_orig = 0.001683, loss_refine = 0.001239[Step 942] loss_orig = 0.002156, loss_refine = 0.004497 [Step 942] loss_orig = 0.001034, loss_refine = 0.001514[Step 942] loss_orig = 0.001331, loss_refine = 0.001521 [Step 942] loss_orig = 0.001932, loss_refine = 0.003494 [Step 942] loss_orig = 0.001303, loss_refine = 0.005712 [Step 942] loss_orig = 0.001864, loss_refine = 0.001372 78%|███████▊ | 943/1208 [12:09:10<3:49:10, 51.89s/it] {'loss': 0.0038, 'grad_norm': 3.2856389578227927, 'learning_rate': 2.1937086092715232e-07, 'completion_length': 107.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.0858154296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.25} 78%|███████▊ | 943/1208 [12:09:10<3:49:10, 51.89s/it]Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1933695: cache has only 0 modules Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1934568: cache has only 0 modules 78%|███████▊ | 944/1208 [12:09:48<3:30:17, 47.79s/it] {'loss': 0.0007, 'grad_norm': 0.09990666901048838, 'learning_rate': 2.185430463576159e-07, 'completion_length': 89.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.01690673828125, 'clip_ratio': 0.0, 'epoch': 6.25} 78%|███████▊ | 944/1208 [12:09:48<3:30:17, 47.79s/it]Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1935441: cache has only 0 modules Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1936314: cache has only 0 modules 78%|███████▊ | 945/1208 [12:10:38<3:32:01, 48.37s/it] {'loss': 0.0016, 'grad_norm': 5.288661676707515, 'learning_rate': 2.1771523178807946e-07, 'completion_length': 123.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.041015625, 'clip_ratio': 0.0, 'epoch': 6.26} 78%|███████▊ | 945/1208 [12:10:38<3:32:01, 48.37s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1937187: cache has only 0 modules Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1938060: cache has only 0 modules 78%|███████▊ | 946/1208 [12:11:21<3:23:59, 46.71s/it] {'loss': 0.0012, 'grad_norm': 0.46932049450738733, 'learning_rate': 2.1688741721854303e-07, 'completion_length': 102.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03057861328125, 'clip_ratio': 0.0, 'epoch': 6.26} 78%|███████▊ | 946/1208 [12:11:21<3:23:59, 46.71s/it]Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1938933: cache has only 0 modules Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1939806: cache has only 0 modules 78%|███████▊ | 947/1208 [12:12:02<3:15:23, 44.92s/it] {'loss': 0.001, 'grad_norm': 12.842042840681387, 'learning_rate': 2.160596026490066e-07, 'completion_length': 102.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0257568359375, 'clip_ratio': 0.0, 'epoch': 6.27} 78%|███████▊ | 947/1208 [12:12:02<3:15:23, 44.92s/it]Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1940679: cache has only 0 modules Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1941552: cache has only 0 modules 78%|███████▊ | 948/1208 [12:12:39<3:04:30, 42.58s/it] {'loss': 0.0044, 'grad_norm': 4.9725929620499665, 'learning_rate': 2.1523178807947017e-07, 'completion_length': 86.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.11083984375, 'clip_ratio': 0.0, 'epoch': 6.28} 78%|███████▊ | 948/1208 [12:12:39<3:04:30, 42.58s/it]Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1942425: cache has only 0 modules Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1943298: cache has only 0 modules 79%|███████▊ | 949/1208 [12:13:18<2:59:23, 41.56s/it] {'loss': 0.0007, 'grad_norm': 0.1566019939787034, 'learning_rate': 2.1440397350993377e-07, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.01690673828125, 'clip_ratio': 0.0, 'epoch': 6.28} 79%|███████▊ | 949/1208 [12:13:18<2:59:23, 41.56s/it]Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1944171: cache has only 0 modules Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1945044: cache has only 0 modules 79%|███████▊ | 950/1208 [12:14:01<3:00:07, 41.89s/it] {'loss': 0.0008, 'grad_norm': 0.22114614267604263, 'learning_rate': 2.1357615894039736e-07, 'completion_length': 110.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0194091796875, 'clip_ratio': 0.0, 'epoch': 6.29} 79%|███████▊ | 950/1208 [12:14:01<3:00:07, 41.89s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1945917: cache has only 0 modules Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1946790: cache has only 0 modules 79%|███████▊ | 951/1208 [12:14:38<2:53:13, 40.44s/it] {'loss': 0.0023, 'grad_norm': 0.7547767848395744, 'learning_rate': 2.1274834437086093e-07, 'completion_length': 93.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.05780029296875, 'clip_ratio': 0.0, 'epoch': 6.3} 79%|███████▊ | 951/1208 [12:14:38<2:53:13, 40.44s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1947663: cache has only 0 modules Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1948536: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1949409: cache has only 0 modules [Step 951] loss_orig = 0.004420, loss_refine = -0.722649 [Step 951] loss_orig = 0.001508, loss_refine = -0.722486[Step 951] loss_orig = 0.001211, loss_refine = -0.722713 [Step 951] loss_orig = 0.001610, loss_refine = 1.208763[Step 951] loss_orig = 0.002272, loss_refine = 1.208868 [Step 951] loss_orig = 0.001097, loss_refine = -0.723177 [Step 951] loss_orig = 0.000854, loss_refine = -0.723406 [Step 951] loss_orig = 0.002878, loss_refine = 1.209432 79%|███████▉ | 952/1208 [12:15:30<3:07:16, 43.89s/it] {'loss': 0.0018, 'grad_norm': 5.6659185466325335, 'learning_rate': 2.119205298013245e-07, 'completion_length': 84.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.05078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 6.3} 79%|███████▉ | 952/1208 [12:15:30<3:07:16, 43.89s/it]Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1950282: cache has only 0 modules Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1951155: cache has only 0 modules 79%|███████▉ | 953/1208 [12:16:11<3:03:29, 43.18s/it] {'loss': 0.0009, 'grad_norm': 14.667363891669858, 'learning_rate': 2.1109271523178807e-07, 'completion_length': 100.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.02142333984375, 'clip_ratio': 0.0, 'epoch': 6.31} 79%|███████▉ | 953/1208 [12:16:11<3:03:29, 43.18s/it]Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1952028: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1952901: cache has only 0 modules 79%|███████▉ | 954/1208 [12:16:55<3:03:23, 43.32s/it] {'loss': 0.0027, 'grad_norm': 5.607361469800782, 'learning_rate': 2.1026490066225164e-07, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.068115234375, 'clip_ratio': 0.0, 'epoch': 6.32} 79%|███████▉ | 954/1208 [12:16:55<3:03:23, 43.32s/it]Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1953774: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. Your previous answer was [{'action': 'click', 'coordinate': [1089, 21]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1954647: cache has only 0 modules [Step 954] loss_orig = 0.001137, loss_refine = -1.497103[Step 954] loss_orig = 0.001586, loss_refine = 0.683614[Step 954] loss_orig = 0.001607, loss_refine = -0.408205[Step 954] loss_orig = 0.001127, loss_refine = -1.499145[Step 954] loss_orig = 0.002384, loss_refine = 0.682900 [Step 954] loss_orig = 0.002108, loss_refine = 0.684131 [Step 954] loss_orig = 0.002068, loss_refine = 0.682774 [Step 954] loss_orig = 0.001022, loss_refine = 0.683081 Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1955520: cache has only 0 modules 79%|███████▉ | 955/1208 [12:17:46<3:12:25, 45.63s/it] {'loss': 0.0014, 'grad_norm': 7.121629073081678, 'learning_rate': 2.094370860927152e-07, 'completion_length': 102.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.45967849095662433, 'kl': 0.03570556640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 6.32} 79%|███████▉ | 955/1208 [12:17:46<3:12:25, 45.63s/it]Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1956393: cache has only 0 modules Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1957266: cache has only 0 modules 79%|███████▉ | 956/1208 [12:18:29<3:09:02, 45.01s/it] {'loss': 0.001, 'grad_norm': 0.5400786421715844, 'learning_rate': 2.086092715231788e-07, 'completion_length': 112.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0240478515625, 'clip_ratio': 0.0, 'epoch': 6.33} 79%|███████▉ | 956/1208 [12:18:29<3:09:02, 45.01s/it]Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1958139: cache has only 0 modules Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1959012: cache has only 0 modules 79%|███████▉ | 957/1208 [12:19:08<3:00:01, 43.03s/it] {'loss': 0.002, 'grad_norm': 1.0332786439951371, 'learning_rate': 2.0778145695364238e-07, 'completion_length': 98.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04931640625, 'clip_ratio': 0.0, 'epoch': 6.34} 79%|███████▉ | 957/1208 [12:19:08<3:00:01, 43.03s/it]Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1959885: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1960758: cache has only 0 modules [Step 957] loss_orig = 0.003810, loss_refine = 0.354601[Step 957] loss_orig = 0.002222, loss_refine = -2.472730[Step 957] loss_orig = 0.001776, loss_refine = 0.355010[Step 957] loss_orig = 0.001435, loss_refine = 0.354452[Step 957] loss_orig = 0.001764, loss_refine = 0.355522 [Step 957] loss_orig = 0.001716, loss_refine = 0.354397 [Step 957] loss_orig = 0.001474, loss_refine = 0.354872 [Step 957] loss_orig = 0.002066, loss_refine = 0.355344 Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1961631: cache has only 0 modules 79%|███████▉ | 958/1208 [12:20:12<3:25:41, 49.37s/it] {'loss': 0.0011, 'grad_norm': 4.898502545145227, 'learning_rate': 2.0695364238410595e-07, 'completion_length': 116.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.11785112818082173, 'kl': 0.03521728515625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 6.34} 79%|███████▉ | 958/1208 [12:20:12<3:25:41, 49.37s/it]Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1962504: cache has only 0 modules Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1963377: cache has only 0 modules 79%|███████▉ | 959/1208 [12:20:55<3:16:35, 47.37s/it] {'loss': 0.0011, 'grad_norm': 4.942145166107587, 'learning_rate': 2.0612582781456952e-07, 'completion_length': 96.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02825927734375, 'clip_ratio': 0.0, 'epoch': 6.35} 79%|███████▉ | 959/1208 [12:20:55<3:16:35, 47.37s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1964250: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1965123: cache has only 0 modules 79%|███████▉ | 960/1208 [12:21:31<3:01:49, 43.99s/it] {'loss': 0.0016, 'grad_norm': 0.25900000880403806, 'learning_rate': 2.052980132450331e-07, 'completion_length': 94.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'epoch': 6.36} 79%|███████▉ | 960/1208 [12:21:31<3:01:49, 43.99s/it]Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1965996: cache has only 0 modules Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1966869: cache has only 0 modules 80%|███████▉ | 961/1208 [12:22:13<2:58:26, 43.35s/it] {'loss': 0.0016, 'grad_norm': 6.230424977701965, 'learning_rate': 2.0447019867549666e-07, 'completion_length': 97.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'epoch': 6.36} 80%|███████▉ | 961/1208 [12:22:13<2:58:26, 43.35s/it]Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1967742: cache has only 0 modules Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1968615: cache has only 0 modules 80%|███████▉ | 962/1208 [12:22:45<2:43:53, 39.98s/it] {'loss': 0.0009, 'grad_norm': 3.911886358138726, 'learning_rate': 2.0364238410596026e-07, 'completion_length': 89.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.02362060546875, 'clip_ratio': 0.0, 'epoch': 6.37} 80%|███████▉ | 962/1208 [12:22:45<2:43:53, 39.98s/it]Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1969488: cache has only 0 modules Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1970361: cache has only 0 modules 80%|███████▉ | 963/1208 [12:23:41<3:03:16, 44.88s/it] {'loss': 0.0013, 'grad_norm': 5.905961879240384, 'learning_rate': 2.0281456953642385e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.7071067541837692, 'kl': 0.03204345703125, 'clip_ratio': 0.0, 'epoch': 6.38} 80%|███████▉ | 963/1208 [12:23:41<3:03:16, 44.88s/it]Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1971234: cache has only 0 modules Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1972107: cache has only 0 modules 80%|███████▉ | 964/1208 [12:24:29<3:06:20, 45.82s/it] {'loss': 0.0017, 'grad_norm': 1.191210625418667, 'learning_rate': 2.0198675496688742e-07, 'completion_length': 98.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0419921875, 'clip_ratio': 0.0, 'epoch': 6.38} 80%|███████▉ | 964/1208 [12:24:29<3:06:20, 45.82s/it]Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1972980: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [457, 450]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1973853: cache has only 0 modules [Step 964] loss_orig = 0.001591, loss_refine = -0.352312[Step 964] loss_orig = 0.001131, loss_refine = -0.351976[Step 964] loss_orig = 0.000634, loss_refine = -0.351198 [Step 964] loss_orig = 0.001775, loss_refine = -0.351311 [Step 964] loss_orig = 0.001793, loss_refine = -0.352140 [Step 964] loss_orig = 0.002122, loss_refine = -0.350468 [Step 964] loss_orig = 0.001387, loss_refine = -0.351258 [Step 964] loss_orig = 0.002235, loss_refine = 2.476968 Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1974726: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1975599: cache has only 0 modules [Step 964] loss_orig = 0.002656, loss_refine = 0.001714 [Step 964] loss_orig = 0.001734, loss_refine = 0.001280[Step 964] loss_orig = 0.001536, loss_refine = 0.000767 [Step 964] loss_orig = 0.001472, loss_refine = 0.000814[Step 964] loss_orig = 0.001178, loss_refine = 0.000798 [Step 964] loss_orig = 0.001675, loss_refine = 0.000702 [Step 964] loss_orig = 0.000979, loss_refine = 0.002036 [Step 964] loss_orig = 0.000807, loss_refine = 0.000502 80%|███████▉ | 965/1208 [12:25:44<3:40:51, 54.53s/it] {'loss': 0.0016, 'grad_norm': 12.48895824654567, 'learning_rate': 2.01158940397351e-07, 'completion_length': 98.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.21875, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.1767766922712326, 'kl': 0.03857421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.4375, 'epoch': 6.39} 80%|███████▉ | 965/1208 [12:25:44<3:40:51, 54.53s/it]Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1976472: cache has only 0 modules Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1977345: cache has only 0 modules 80%|███████▉ | 966/1208 [12:26:30<3:29:44, 52.00s/it] {'loss': 0.0013, 'grad_norm': 6.523763441881544, 'learning_rate': 2.0033112582781456e-07, 'completion_length': 101.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.03240966796875, 'clip_ratio': 0.0, 'epoch': 6.4} 80%|███████▉ | 966/1208 [12:26:30<3:29:44, 52.00s/it]Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1978218: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1979091: cache has only 0 modules 80%|████████ | 967/1208 [12:27:13<3:18:22, 49.39s/it] {'loss': 0.002, 'grad_norm': 8.424984194512382, 'learning_rate': 1.9950331125827813e-07, 'completion_length': 103.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.05078125, 'clip_ratio': 0.0, 'epoch': 6.4} 80%|████████ | 967/1208 [12:27:13<3:18:22, 49.39s/it]Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1979964: cache has only 0 modules Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1980837: cache has only 0 modules 80%|████████ | 968/1208 [12:27:55<3:07:52, 46.97s/it] {'loss': 0.0013, 'grad_norm': 5.754528928845356, 'learning_rate': 1.986754966887417e-07, 'completion_length': 104.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03179931640625, 'clip_ratio': 0.0, 'epoch': 6.41} 80%|████████ | 968/1208 [12:27:55<3:07:52, 46.97s/it]Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1981710: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1982583: cache has only 0 modules 80%|████████ | 969/1208 [12:28:33<2:57:13, 44.49s/it] {'loss': 0.0013, 'grad_norm': 19.932834842096543, 'learning_rate': 1.978476821192053e-07, 'completion_length': 96.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.032470703125, 'clip_ratio': 0.0, 'epoch': 6.42} 80%|████████ | 969/1208 [12:28:33<2:57:13, 44.49s/it]Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1983456: cache has only 0 modules Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1984329: cache has only 0 modules 80%|████████ | 970/1208 [12:29:13<2:51:19, 43.19s/it] {'loss': 0.0015, 'grad_norm': 15.229349394858538, 'learning_rate': 1.9701986754966887e-07, 'completion_length': 117.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.03839111328125, 'clip_ratio': 0.0, 'epoch': 6.42} 80%|████████ | 970/1208 [12:29:13<2:51:19, 43.19s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1985202: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element amazon - Search'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1986075: cache has only 0 modules [Step 970] loss_orig = 0.001354, loss_refine = 0.003157[Step 970] loss_orig = 0.004087, loss_refine = 0.002442[Step 970] loss_orig = 0.001206, loss_refine = 0.002013[Step 970] loss_orig = 0.001226, loss_refine = 0.000538 [Step 970] loss_orig = 0.001308, loss_refine = 0.001891 [Step 970] loss_orig = 0.002086, loss_refine = 0.001277[Step 970] loss_orig = 0.001270, loss_refine = 0.001217 [Step 970] loss_orig = 0.001390, loss_refine = 0.002615 Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1986948: cache has only 0 modules 80%|████████ | 971/1208 [12:30:06<3:01:13, 45.88s/it] {'loss': 0.0013, 'grad_norm': 0.5376598566163936, 'learning_rate': 1.9619205298013244e-07, 'completion_length': 101.45833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.03021240234375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 6.43} 80%|████████ | 971/1208 [12:30:06<3:01:13, 45.88s/it]Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1987821: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1988694: cache has only 0 modules 80%|████████ | 972/1208 [12:30:57<3:07:09, 47.58s/it] {'loss': 0.0016, 'grad_norm': 17.510326513053087, 'learning_rate': 1.95364238410596e-07, 'completion_length': 119.5, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.6722923070192337, 'kl': 0.03875732421875, 'clip_ratio': 0.0, 'epoch': 6.44} 80%|████████ | 972/1208 [12:30:57<3:07:09, 47.58s/it]Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1989567: cache has only 0 modules Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1990440: cache has only 0 modules 81%|████████ | 973/1208 [12:31:38<2:59:00, 45.70s/it] {'loss': 0.0018, 'grad_norm': 0.22041770002258942, 'learning_rate': 1.9453642384105958e-07, 'completion_length': 102.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.044189453125, 'clip_ratio': 0.0, 'epoch': 6.44} 81%|████████ | 973/1208 [12:31:39<2:59:00, 45.70s/it]Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1991313: cache has only 0 modules Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1992186: cache has only 0 modules 81%|████████ | 974/1208 [12:32:13<2:45:20, 42.40s/it] {'loss': 0.0023, 'grad_norm': 8.983159635790262, 'learning_rate': 1.9370860927152315e-07, 'completion_length': 97.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.44403792917728424, 'kl': 0.0565185546875, 'clip_ratio': 0.0, 'epoch': 6.45} 81%|████████ | 974/1208 [12:32:13<2:45:20, 42.40s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1993059: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1993932: cache has only 0 modules 81%|████████ | 975/1208 [12:32:54<2:43:23, 42.08s/it] {'loss': 0.0016, 'grad_norm': 11.884867512616063, 'learning_rate': 1.9288079470198677e-07, 'completion_length': 101.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'epoch': 6.46} 81%|████████ | 975/1208 [12:32:55<2:43:23, 42.08s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1994805: cache has only 0 modules Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1995678: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. Your previous answer was [{'action': 'click', 'coordinate': [2211, 49]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 1996551: cache has only 0 modules [Step 975] loss_orig = 0.002290, loss_refine = 0.000909[Step 975] loss_orig = 0.002792, loss_refine = 0.001396 [Step 975] loss_orig = 0.008941, loss_refine = 0.003303[Step 975] loss_orig = 0.002292, loss_refine = 0.001620 [Step 975] loss_orig = 0.001449, loss_refine = 0.001129 [Step 975] loss_orig = 0.002032, loss_refine = 0.001240 [Step 975] loss_orig = 0.001948, loss_refine = 0.001805 [Step 975] loss_orig = 0.001483, loss_refine = 0.002110 81%|████████ | 976/1208 [12:33:56<3:05:12, 47.90s/it] {'loss': 0.0018, 'grad_norm': 14.917357962729398, 'learning_rate': 1.9205298013245034e-07, 'completion_length': 104.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.11785112818082173, 'kl': 0.06005859375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 6.46} 81%|████████ | 976/1208 [12:33:56<3:05:12, 47.90s/it]Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1997424: cache has only 0 modules Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1998297: cache has only 0 modules 81%|████████ | 977/1208 [12:34:36<2:54:50, 45.41s/it] {'loss': 0.0029, 'grad_norm': 8.022246831675862, 'learning_rate': 1.912251655629139e-07, 'completion_length': 100.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0716552734375, 'clip_ratio': 0.0, 'epoch': 6.47} 81%|████████ | 977/1208 [12:34:36<2:54:50, 45.41s/it]Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 1999170: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2000043: cache has only 0 modules 81%|████████ | 978/1208 [12:35:15<2:46:50, 43.53s/it] {'loss': 0.0034, 'grad_norm': 25.603833823283214, 'learning_rate': 1.9039735099337748e-07, 'completion_length': 105.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.1875, 'rewards/format_reward': 1.0, 'reward': 2.1875, 'reward_std': 0.408231720328331, 'kl': 0.083984375, 'clip_ratio': 0.0, 'epoch': 6.48} 81%|████████ | 978/1208 [12:35:15<2:46:50, 43.53s/it]Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2000916: cache has only 0 modules Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2001789: cache has only 0 modules 81%|████████ | 979/1208 [12:36:03<2:52:08, 45.10s/it] {'loss': 0.0012, 'grad_norm': 5.841583039592695, 'learning_rate': 1.8956953642384105e-07, 'completion_length': 104.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.7071067541837692, 'kl': 0.02972412109375, 'clip_ratio': 0.0, 'epoch': 6.48} 81%|████████ | 979/1208 [12:36:04<2:52:08, 45.10s/it]Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2002662: cache has only 0 modules Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2003535: cache has only 0 modules 81%|████████ | 980/1208 [12:36:43<2:45:09, 43.46s/it] {'loss': 0.0015, 'grad_norm': 5.759291351023975, 'learning_rate': 1.8874172185430462e-07, 'completion_length': 90.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.03704833984375, 'clip_ratio': 0.0, 'epoch': 6.49} 81%|████████ | 980/1208 [12:36:43<2:45:09, 43.46s/it]Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2004408: cache has only 0 modules Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2005281: cache has only 0 modules 81%|████████ | 981/1208 [12:37:19<2:35:42, 41.16s/it] {'loss': 0.0014, 'grad_norm': 0.5028679192287636, 'learning_rate': 1.879139072847682e-07, 'completion_length': 83.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0357666015625, 'clip_ratio': 0.0, 'epoch': 6.5} 81%|████████ | 981/1208 [12:37:19<2:35:42, 41.16s/it]Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2006154: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2007027: cache has only 0 modules 81%|████████▏ | 982/1208 [12:37:54<2:28:20, 39.38s/it] {'loss': 0.0025, 'grad_norm': 5.5882401491696365, 'learning_rate': 1.870860927152318e-07, 'completion_length': 89.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.0625, 'clip_ratio': 0.0, 'epoch': 6.5} 81%|████████▏ | 982/1208 [12:37:54<2:28:20, 39.38s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2007900: cache has only 0 modules Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2008773: cache has only 0 modules 81%|████████▏ | 983/1208 [12:38:30<2:23:55, 38.38s/it] {'loss': 0.0026, 'grad_norm': 0.33444554838975243, 'learning_rate': 1.8625827814569536e-07, 'completion_length': 88.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.06573486328125, 'clip_ratio': 0.0, 'epoch': 6.51} 81%|████████▏ | 983/1208 [12:38:30<2:23:55, 38.38s/it]Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2009646: cache has only 0 modules Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2010519: cache has only 0 modules 81%|████████▏ | 984/1208 [12:39:11<2:25:46, 39.04s/it] {'loss': 0.0018, 'grad_norm': 5.306614304391838, 'learning_rate': 1.8543046357615893e-07, 'completion_length': 100.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.045166015625, 'clip_ratio': 0.0, 'epoch': 6.52} 81%|████████▏ | 984/1208 [12:39:11<2:25:46, 39.04s/it]Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2011392: cache has only 0 modules Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2012265: cache has only 0 modules 82%|████████▏ | 985/1208 [12:39:55<2:31:21, 40.73s/it] {'loss': 0.0016, 'grad_norm': 5.285341395917217, 'learning_rate': 1.846026490066225e-07, 'completion_length': 111.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.04095458984375, 'clip_ratio': 0.0, 'epoch': 6.52} 82%|████████▏ | 985/1208 [12:39:55<2:31:21, 40.73s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2013138: cache has only 0 modules Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2014011: cache has only 0 modules 82%|████████▏ | 986/1208 [12:40:40<2:34:34, 41.78s/it] {'loss': 0.0017, 'grad_norm': 10.429204143512004, 'learning_rate': 1.8377483443708607e-07, 'completion_length': 102.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.0428466796875, 'clip_ratio': 0.0, 'epoch': 6.53} 82%|████████▏ | 986/1208 [12:40:40<2:34:34, 41.78s/it]Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2014884: cache has only 0 modules Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2015757: cache has only 0 modules 82%|████████▏ | 987/1208 [12:41:17<2:28:35, 40.34s/it] {'loss': 0.0021, 'grad_norm': 3.8002854895232585, 'learning_rate': 1.8294701986754964e-07, 'completion_length': 97.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0516357421875, 'clip_ratio': 0.0, 'epoch': 6.54} 82%|████████▏ | 987/1208 [12:41:17<2:28:35, 40.34s/it]Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2016630: cache has only 0 modules Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2017503: cache has only 0 modules 82%|████████▏ | 988/1208 [12:41:47<2:17:01, 37.37s/it] {'loss': 0.0018, 'grad_norm': 5.943597165129123, 'learning_rate': 1.821192052980132e-07, 'completion_length': 73.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0439453125, 'clip_ratio': 0.0, 'epoch': 6.54} 82%|████████▏ | 988/1208 [12:41:47<2:17:01, 37.37s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2018376: cache has only 0 modules Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2019249: cache has only 0 modules 82%|████████▏ | 989/1208 [12:42:29<2:21:20, 38.72s/it] {'loss': 0.0012, 'grad_norm': 16.177492619819187, 'learning_rate': 1.8129139072847683e-07, 'completion_length': 95.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.02886962890625, 'clip_ratio': 0.0, 'epoch': 6.55} 82%|████████▏ | 989/1208 [12:42:29<2:21:20, 38.72s/it]Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2020122: cache has only 0 modules Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2020995: cache has only 0 modules 82%|████████▏ | 990/1208 [12:43:05<2:18:09, 38.02s/it] {'loss': 0.0031, 'grad_norm': 8.182129455887681, 'learning_rate': 1.804635761589404e-07, 'completion_length': 101.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.07763671875, 'clip_ratio': 0.0, 'epoch': 6.56} 82%|████████▏ | 990/1208 [12:43:05<2:18:09, 38.02s/it]Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2021868: cache has only 0 modules Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2022741: cache has only 0 modules 82%|████████▏ | 991/1208 [12:43:45<2:19:40, 38.62s/it] {'loss': 0.0012, 'grad_norm': 5.157040733349701, 'learning_rate': 1.7963576158940397e-07, 'completion_length': 97.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0291748046875, 'clip_ratio': 0.0, 'epoch': 6.56} 82%|████████▏ | 991/1208 [12:43:45<2:19:40, 38.62s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2023614: cache has only 0 modules Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2024487: cache has only 0 modules 82%|████████▏ | 992/1208 [12:44:21<2:15:41, 37.69s/it] {'loss': 0.0039, 'grad_norm': 10.230736747197716, 'learning_rate': 1.7880794701986754e-07, 'completion_length': 85.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.097900390625, 'clip_ratio': 0.0, 'epoch': 6.57} 82%|████████▏ | 992/1208 [12:44:21<2:15:41, 37.69s/it]Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2025360: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: topic-link-a151002'. Your previous answer was [{'action': 'click', 'coordinate': [1736, 487]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2026233: cache has only 0 modules [Step 992] loss_orig = 0.001718, loss_refine = 0.661880 [Step 992] loss_orig = 0.001679, loss_refine = -1.982417[Step 992] loss_orig = 0.000987, loss_refine = 0.662402 [Step 992] loss_orig = 0.000810, loss_refine = -0.659531 [Step 992] loss_orig = 0.000673, loss_refine = 0.663216 [Step 992] loss_orig = 0.001233, loss_refine = -0.660612 [Step 992] loss_orig = 0.001323, loss_refine = 0.661926 [Step 992] loss_orig = 0.001540, loss_refine = 0.662550 Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2027106: cache has only 0 modules 82%|████████▏ | 993/1208 [12:45:18<2:36:25, 43.65s/it] {'loss': 0.0013, 'grad_norm': 13.27575024509105, 'learning_rate': 1.7798013245033111e-07, 'completion_length': 98.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.2519763112068176, 'kl': 0.03289794921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 6.58} 82%|████████▏ | 993/1208 [12:45:18<2:36:25, 43.65s/it]Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2027979: cache has only 0 modules Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2028852: cache has only 0 modules 82%|████████▏ | 994/1208 [12:46:01<2:34:33, 43.33s/it] {'loss': 0.0032, 'grad_norm': 51.9915703573761, 'learning_rate': 1.7715231788079468e-07, 'completion_length': 107.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.07958984375, 'clip_ratio': 0.0, 'epoch': 6.58} 82%|████████▏ | 994/1208 [12:46:01<2:34:33, 43.33s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2029725: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2030598: cache has only 0 modules [Step 994] loss_orig = 0.001193, loss_refine = -0.350694[Step 994] loss_orig = 0.000561, loss_refine = -0.351638[Step 994] loss_orig = 0.001019, loss_refine = 2.475770 [Step 994] loss_orig = 0.001639, loss_refine = -0.351412[Step 994] loss_orig = 0.001663, loss_refine = -0.350833 [Step 994] loss_orig = 0.000772, loss_refine = -0.351912 [Step 994] loss_orig = 0.000709, loss_refine = -0.351207 [Step 994] loss_orig = 0.000920, loss_refine = -0.352989 Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2031471: cache has only 0 modules 82%|████████▏ | 995/1208 [12:46:55<2:45:06, 46.51s/it] {'loss': 0.0015, 'grad_norm': 15.815077228489358, 'learning_rate': 1.7632450331125828e-07, 'completion_length': 89.70833333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.11785112818082173, 'kl': 0.02606201171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 6.59} 82%|████████▏ | 995/1208 [12:46:55<2:45:06, 46.51s/it]Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2032344: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2033217: cache has only 0 modules 82%|████████▏ | 996/1208 [12:47:32<2:34:47, 43.81s/it] {'loss': 0.0019, 'grad_norm': 0.43373046155938577, 'learning_rate': 1.7549668874172185e-07, 'completion_length': 91.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0482177734375, 'clip_ratio': 0.0, 'epoch': 6.6} 82%|████████▏ | 996/1208 [12:47:32<2:34:47, 43.81s/it]Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2034090: cache has only 0 modules Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2034963: cache has only 0 modules 83%|████████▎ | 997/1208 [12:48:09<2:26:07, 41.55s/it] {'loss': 0.0009, 'grad_norm': 4.7304049922108, 'learning_rate': 1.7466887417218542e-07, 'completion_length': 91.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02252197265625, 'clip_ratio': 0.0, 'epoch': 6.6} 83%|████████▎ | 997/1208 [12:48:09<2:26:07, 41.55s/it]Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2035836: cache has only 0 modules Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2036709: cache has only 0 modules 83%|████████▎ | 998/1208 [12:48:44<2:19:03, 39.73s/it] {'loss': 0.0017, 'grad_norm': 7.770598057425113, 'learning_rate': 1.73841059602649e-07, 'completion_length': 89.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.49871626496315, 'kl': 0.04150390625, 'clip_ratio': 0.0, 'epoch': 6.61} 83%|████████▎ | 998/1208 [12:48:44<2:19:03, 39.73s/it]Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2037582: cache has only 0 modules Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2038455: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [1011, 964] }] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2039328: cache has only 0 modules [Step 998] loss_orig = 0.001091, loss_refine = -0.722919[Step 998] loss_orig = 0.005941, loss_refine = 1.208822 [Step 998] loss_orig = 0.001066, loss_refine = 1.208357 [Step 998] loss_orig = 0.001686, loss_refine = 1.208125[Step 998] loss_orig = 0.001423, loss_refine = -0.723500 [Step 998] loss_orig = 0.002129, loss_refine = -0.722824 [Step 998] loss_orig = 0.002207, loss_refine = -0.723408 [Step 998] loss_orig = 0.001903, loss_refine = -0.723447 83%|████████▎ | 999/1208 [12:49:38<2:33:14, 43.99s/it] {'loss': 0.0013, 'grad_norm': 4.836287016687006, 'learning_rate': 1.7301324503311256e-07, 'completion_length': 86.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0445556640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 6.62} 83%|████████▎ | 999/1208 [12:49:38<2:33:14, 43.99s/it]Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2040201: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2041074: cache has only 0 modules 83%|████████▎ | 1000/1208 [12:50:21<2:31:10, 43.61s/it] {'loss': 0.0026, 'grad_norm': 12.213443768929812, 'learning_rate': 1.7218543046357613e-07, 'completion_length': 104.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.4355512708425522, 'kl': 0.06396484375, 'clip_ratio': 0.0, 'epoch': 6.62} 83%|████████▎ | 1000/1208 [12:50:21<2:31:10, 43.61s/it]Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2041947: cache has only 0 modules Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2042820: cache has only 0 modules 83%|████████▎ | 1001/1208 [12:51:04<2:29:44, 43.41s/it] {'loss': 0.0017, 'grad_norm': 8.197360724610652, 'learning_rate': 1.7135761589403973e-07, 'completion_length': 101.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04327392578125, 'clip_ratio': 0.0, 'epoch': 6.63} 83%|████████▎ | 1001/1208 [12:51:04<2:29:44, 43.41s/it]Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2043693: cache has only 0 modules Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2044566: cache has only 0 modules 83%|████████▎ | 1002/1208 [12:51:42<2:23:38, 41.84s/it] {'loss': 0.0011, 'grad_norm': 0.25235852285690125, 'learning_rate': 1.7052980132450332e-07, 'completion_length': 92.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02667236328125, 'clip_ratio': 0.0, 'epoch': 6.64} 83%|████████▎ | 1002/1208 [12:51:42<2:23:38, 41.84s/it]Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2045439: cache has only 0 modules Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2046312: cache has only 0 modules 83%|████████▎ | 1003/1208 [12:52:21<2:19:42, 40.89s/it] {'loss': 0.0029, 'grad_norm': 15.749793542280699, 'learning_rate': 1.697019867549669e-07, 'completion_length': 94.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.49022960662841797, 'kl': 0.0714111328125, 'clip_ratio': 0.0, 'epoch': 6.64} 83%|████████▎ | 1003/1208 [12:52:21<2:19:42, 40.89s/it]Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2047185: cache has only 0 modules Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2048058: cache has only 0 modules 83%|████████▎ | 1004/1208 [12:53:06<2:23:31, 42.22s/it] {'loss': 0.0019, 'grad_norm': 7.472365740065176, 'learning_rate': 1.6887417218543046e-07, 'completion_length': 105.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.49022960662841797, 'kl': 0.048095703125, 'clip_ratio': 0.0, 'epoch': 6.65} 83%|████████▎ | 1004/1208 [12:53:06<2:23:31, 42.22s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2048931: cache has only 0 modules Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2049804: cache has only 0 modules 83%|████████▎ | 1005/1208 [12:53:55<2:29:25, 44.17s/it] {'loss': 0.0009, 'grad_norm': 4.0690830864912595, 'learning_rate': 1.6804635761589403e-07, 'completion_length': 111.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02166748046875, 'clip_ratio': 0.0, 'epoch': 6.66} 83%|████████▎ | 1005/1208 [12:53:55<2:29:25, 44.17s/it]Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2050677: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. Your previous answer was [{'action': 'click', 'coordinate': [1128, 104]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2051550: cache has only 0 modules [Step 1005] loss_orig = 0.002870, loss_refine = 0.543751[Step 1005] loss_orig = 0.002581, loss_refine = -1.618058[Step 1005] loss_orig = 0.001619, loss_refine = 0.541041 [Step 1005] loss_orig = 0.003336, loss_refine = 0.541238 [Step 1005] loss_orig = 0.001004, loss_refine = 0.543317 [Step 1005] loss_orig = 0.002064, loss_refine = 0.540946[Step 1005] loss_orig = 0.004568, loss_refine = -1.618890 [Step 1005] loss_orig = 0.001842, loss_refine = 0.541277 Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2052423: cache has only 0 modules 83%|████████▎ | 1006/1208 [12:54:50<2:39:49, 47.47s/it] {'loss': 0.0019, 'grad_norm': 8.41917608800719, 'learning_rate': 1.672185430463576e-07, 'completion_length': 97.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.15430335203806558, 'kl': 0.0556640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 6.66} 83%|████████▎ | 1006/1208 [12:54:50<2:39:49, 47.47s/it]Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2053296: cache has only 0 modules Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2054169: cache has only 0 modules 83%|████████▎ | 1007/1208 [12:55:30<2:31:25, 45.20s/it] {'loss': 0.0013, 'grad_norm': 9.032312727757215, 'learning_rate': 1.6639072847682117e-07, 'completion_length': 96.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0325927734375, 'clip_ratio': 0.0, 'epoch': 6.67} 83%|████████▎ | 1007/1208 [12:55:30<2:31:25, 45.20s/it]Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2055042: cache has only 0 modules Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2055915: cache has only 0 modules 83%|████████▎ | 1008/1208 [12:56:13<2:28:31, 44.56s/it] {'loss': 0.0011, 'grad_norm': 3.1377925877886774, 'learning_rate': 1.6556291390728477e-07, 'completion_length': 96.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.026611328125, 'clip_ratio': 0.0, 'epoch': 6.68} 83%|████████▎ | 1008/1208 [12:56:13<2:28:31, 44.56s/it]Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2056788: cache has only 0 modules Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2057661: cache has only 0 modules 84%|████████▎ | 1009/1208 [12:56:50<2:20:03, 42.23s/it] {'loss': 0.0019, 'grad_norm': 6.677825310604539, 'learning_rate': 1.6473509933774834e-07, 'completion_length': 98.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0469970703125, 'clip_ratio': 0.0, 'epoch': 6.68} 84%|████████▎ | 1009/1208 [12:56:50<2:20:03, 42.23s/it]Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2058534: cache has only 0 modules Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2059407: cache has only 0 modules 84%|████████▎ | 1010/1208 [12:57:38<2:25:33, 44.11s/it] {'loss': 0.0022, 'grad_norm': 8.83086580840498, 'learning_rate': 1.639072847682119e-07, 'completion_length': 107.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.9375, 'reward': 2.25, 'reward_std': 0.6760360598564148, 'kl': 0.0556640625, 'clip_ratio': 0.0, 'epoch': 6.69} 84%|████████▎ | 1010/1208 [12:57:38<2:25:33, 44.11s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2060280: cache has only 0 modules Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2061153: cache has only 0 modules 84%|████████▎ | 1011/1208 [12:58:13<2:15:59, 41.42s/it] {'loss': 0.0031, 'grad_norm': 0.4348356373618437, 'learning_rate': 1.6307947019867548e-07, 'completion_length': 90.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.077880859375, 'clip_ratio': 0.0, 'epoch': 6.7} 84%|████████▎ | 1011/1208 [12:58:13<2:15:59, 41.42s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2062026: cache has only 0 modules Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2062899: cache has only 0 modules 84%|████████▍ | 1012/1208 [12:59:05<2:25:25, 44.52s/it] {'loss': 0.0013, 'grad_norm': 5.722206633412313, 'learning_rate': 1.6225165562913905e-07, 'completion_length': 103.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.9375, 'reward': 2.75, 'reward_std': 0.5345224738121033, 'kl': 0.0318603515625, 'clip_ratio': 0.0, 'epoch': 6.7} 84%|████████▍ | 1012/1208 [12:59:05<2:25:25, 44.52s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2063772: cache has only 0 modules Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2064645: cache has only 0 modules 84%|████████▍ | 1013/1208 [12:59:49<2:23:55, 44.29s/it] {'loss': 0.002, 'grad_norm': 3.8078855201152573, 'learning_rate': 1.6142384105960262e-07, 'completion_length': 112.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.049072265625, 'clip_ratio': 0.0, 'epoch': 6.71} 84%|████████▍ | 1013/1208 [12:59:49<2:23:55, 44.29s/it]Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2065518: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2066391: cache has only 0 modules 84%|████████▍ | 1014/1208 [13:00:24<2:14:38, 41.64s/it] {'loss': 0.0008, 'grad_norm': 0.13842960073613658, 'learning_rate': 1.6059602649006622e-07, 'completion_length': 90.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02008056640625, 'clip_ratio': 0.0, 'epoch': 6.72} 84%|████████▍ | 1014/1208 [13:00:24<2:14:38, 41.64s/it]Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2067264: cache has only 0 modules Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2068137: cache has only 0 modules 84%|████████▍ | 1015/1208 [13:01:14<2:21:38, 44.03s/it] {'loss': 0.0017, 'grad_norm': 8.84301949061208, 'learning_rate': 1.5976821192052981e-07, 'completion_length': 111.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.04168701171875, 'clip_ratio': 0.0, 'epoch': 6.72} 84%|████████▍ | 1015/1208 [13:01:14<2:21:38, 44.03s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2069010: cache has only 0 modules Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2069883: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2070756: cache has only 0 modules [Step 1015] loss_orig = -0.352613, loss_refine = -0.352354[Step 1015] loss_orig = -0.352961, loss_refine = -0.352533 [Step 1015] loss_orig = -0.352359, loss_refine = 2.475377 [Step 1015] loss_orig = 2.477867, loss_refine = -0.352304 [Step 1015] loss_orig = -0.352647, loss_refine = -0.352541 [Step 1015] loss_orig = -0.349863, loss_refine = -0.349306[Step 1015] loss_orig = -0.351279, loss_refine = -0.350126 [Step 1015] loss_orig = -0.353055, loss_refine = -0.352436 84%|████████▍ | 1016/1208 [13:02:16<2:38:28, 49.52s/it] {'loss': 0.0022, 'grad_norm': 12.600048688876566, 'learning_rate': 1.5894039735099338e-07, 'completion_length': 118.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.16666666666666666, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.4166666666666665, 'reward_std': 0.41387641429901123, 'kl': 0.0548095703125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 6.73} 84%|████████▍ | 1016/1208 [13:02:16<2:38:28, 49.52s/it]Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2071629: cache has only 0 modules Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2072502: cache has only 0 modules 84%|████████▍ | 1017/1208 [13:02:58<2:30:17, 47.21s/it] {'loss': 0.0014, 'grad_norm': 5.398580001097086, 'learning_rate': 1.5811258278145695e-07, 'completion_length': 93.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03582763671875, 'clip_ratio': 0.0, 'epoch': 6.74} 84%|████████▍ | 1017/1208 [13:02:58<2:30:17, 47.21s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2073375: cache has only 0 modules Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2074248: cache has only 0 modules 84%|████████▍ | 1018/1208 [13:03:40<2:24:47, 45.72s/it] {'loss': 0.0009, 'grad_norm': 0.3232589998204409, 'learning_rate': 1.5728476821192052e-07, 'completion_length': 89.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.022918701171875, 'clip_ratio': 0.0, 'epoch': 6.74} 84%|████████▍ | 1018/1208 [13:03:40<2:24:47, 45.72s/it]Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2075121: cache has only 0 modules Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2075994: cache has only 0 modules 84%|████████▍ | 1019/1208 [13:04:14<2:12:13, 41.98s/it] {'loss': 0.0008, 'grad_norm': 6.6702608296545955, 'learning_rate': 1.564569536423841e-07, 'completion_length': 86.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.01995849609375, 'clip_ratio': 0.0, 'epoch': 6.75} 84%|████████▍ | 1019/1208 [13:04:14<2:12:13, 41.98s/it]Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2076867: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2077740: cache has only 0 modules 84%|████████▍ | 1020/1208 [13:05:03<2:18:59, 44.36s/it] {'loss': 0.0021, 'grad_norm': 17.120382015285127, 'learning_rate': 1.5562913907284766e-07, 'completion_length': 109.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.4629100561141968, 'kl': 0.0521240234375, 'clip_ratio': 0.0, 'epoch': 6.75} 84%|████████▍ | 1020/1208 [13:05:03<2:18:59, 44.36s/it]Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2078613: cache has only 0 modules Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2079486: cache has only 0 modules 85%|████████▍ | 1021/1208 [13:05:47<2:17:40, 44.17s/it] {'loss': 0.0013, 'grad_norm': 30.55281671176761, 'learning_rate': 1.5480132450331126e-07, 'completion_length': 98.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.03265380859375, 'clip_ratio': 0.0, 'epoch': 6.76} 85%|████████▍ | 1021/1208 [13:05:47<2:17:40, 44.17s/it]Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2080359: cache has only 0 modules Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2081232: cache has only 0 modules 85%|████████▍ | 1022/1208 [13:06:26<2:12:24, 42.71s/it] {'loss': 0.0011, 'grad_norm': 1.3280844555456581, 'learning_rate': 1.5397350993377483e-07, 'completion_length': 101.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0272216796875, 'clip_ratio': 0.0, 'epoch': 6.77} 85%|████████▍ | 1022/1208 [13:06:26<2:12:24, 42.71s/it]Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2082105: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2082978: cache has only 0 modules 85%|████████▍ | 1023/1208 [13:07:03<2:05:49, 40.81s/it] {'loss': 0.0029, 'grad_norm': 0.2644976350764798, 'learning_rate': 1.531456953642384e-07, 'completion_length': 83.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.07305908203125, 'clip_ratio': 0.0, 'epoch': 6.77} 85%|████████▍ | 1023/1208 [13:07:03<2:05:49, 40.81s/it]Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2083851: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2084724: cache has only 0 modules 85%|████████▍ | 1024/1208 [13:07:36<1:58:12, 38.55s/it] {'loss': 0.002, 'grad_norm': 0.4518096057361113, 'learning_rate': 1.5231788079470197e-07, 'completion_length': 96.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.051025390625, 'clip_ratio': 0.0, 'epoch': 6.78} 85%|████████▍ | 1024/1208 [13:07:36<1:58:12, 38.55s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2085597: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2086470: cache has only 0 modules 85%|████████▍ | 1025/1208 [13:08:20<2:02:10, 40.06s/it] {'loss': 0.0033, 'grad_norm': 8.473449458612926, 'learning_rate': 1.5149006622516554e-07, 'completion_length': 91.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.082275390625, 'clip_ratio': 0.0, 'epoch': 6.79} 85%|████████▍ | 1025/1208 [13:08:20<2:02:10, 40.06s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2087343: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2088216: cache has only 0 modules 85%|████████▍ | 1026/1208 [13:08:56<1:57:56, 38.88s/it] {'loss': 0.0012, 'grad_norm': 0.3596943228826575, 'learning_rate': 1.5066225165562914e-07, 'completion_length': 87.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.028900146484375, 'clip_ratio': 0.0, 'epoch': 6.79} 85%|████████▍ | 1026/1208 [13:08:56<1:57:56, 38.88s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2089089: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2089962: cache has only 0 modules 85%|████████▌ | 1027/1208 [13:09:40<2:02:19, 40.55s/it] {'loss': 0.0019, 'grad_norm': 4.194391468227118, 'learning_rate': 1.498344370860927e-07, 'completion_length': 105.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.04632568359375, 'clip_ratio': 0.0, 'epoch': 6.8} 85%|████████▌ | 1027/1208 [13:09:40<2:02:19, 40.55s/it]Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2090835: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2091708: cache has only 0 modules 85%|████████▌ | 1028/1208 [13:10:13<1:54:39, 38.22s/it] {'loss': 0.001, 'grad_norm': 0.2741645855479266, 'learning_rate': 1.490066225165563e-07, 'completion_length': 82.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02508544921875, 'clip_ratio': 0.0, 'epoch': 6.81} 85%|████████▌ | 1028/1208 [13:10:13<1:54:39, 38.22s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2092581: cache has only 0 modules Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2093454: cache has only 0 modules 85%|████████▌ | 1029/1208 [13:10:51<1:53:24, 38.01s/it] {'loss': 0.0015, 'grad_norm': 4.90442106475926, 'learning_rate': 1.4817880794701987e-07, 'completion_length': 96.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03717041015625, 'clip_ratio': 0.0, 'epoch': 6.81} 85%|████████▌ | 1029/1208 [13:10:51<1:53:24, 38.01s/it]Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2094327: cache has only 0 modules Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2095200: cache has only 0 modules 85%|████████▌ | 1030/1208 [13:11:39<2:02:18, 41.22s/it] {'loss': 0.0024, 'grad_norm': 10.127227662662122, 'learning_rate': 1.4735099337748344e-07, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0589599609375, 'clip_ratio': 0.0, 'epoch': 6.82} 85%|████████▌ | 1030/1208 [13:11:39<2:02:18, 41.22s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2096073: cache has only 0 modules Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2096946: cache has only 0 modules 85%|████████▌ | 1031/1208 [13:12:20<2:01:34, 41.21s/it] {'loss': 0.0014, 'grad_norm': 0.2108213810861725, 'learning_rate': 1.4652317880794701e-07, 'completion_length': 102.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0360107421875, 'clip_ratio': 0.0, 'epoch': 6.83} 85%|████████▌ | 1031/1208 [13:12:20<2:01:34, 41.21s/it]Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2097819: cache has only 0 modules Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2098692: cache has only 0 modules 85%|████████▌ | 1032/1208 [13:12:55<1:54:39, 39.09s/it] {'loss': 0.0008, 'grad_norm': 0.15783682373632008, 'learning_rate': 1.4569536423841058e-07, 'completion_length': 91.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0189208984375, 'clip_ratio': 0.0, 'epoch': 6.83} 85%|████████▌ | 1032/1208 [13:12:55<1:54:39, 39.09s/it]Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2099565: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2100438: cache has only 0 modules [Step 1032] loss_orig = 0.000624, loss_refine = -0.345759[Step 1032] loss_orig = 0.000726, loss_refine = -0.352695 [Step 1032] loss_orig = 0.000745, loss_refine = -0.350781 [Step 1032] loss_orig = 0.001220, loss_refine = -0.351210[Step 1032] loss_orig = 0.001127, loss_refine = 2.475704 [Step 1032] loss_orig = 0.000815, loss_refine = -0.352301 [Step 1032] loss_orig = 0.000561, loss_refine = -0.351233 [Step 1032] loss_orig = 0.000810, loss_refine = -0.350109 Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2101311: cache has only 0 modules 86%|████████▌ | 1033/1208 [13:13:47<2:06:00, 43.20s/it] {'loss': 0.0016, 'grad_norm': 3.4758443383436677, 'learning_rate': 1.4486754966887415e-07, 'completion_length': 87.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.9583333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.016571044921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.84} 86%|████████▌ | 1033/1208 [13:13:47<2:06:00, 43.20s/it]Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2102184: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2103057: cache has only 0 modules [Step 1033] loss_orig = 0.002074, loss_refine = 0.244364[Step 1033] loss_orig = 0.000946, loss_refine = -0.722998 [Step 1033] loss_orig = 0.002797, loss_refine = -0.719075 [Step 1033] loss_orig = 0.003290, loss_refine = 0.244230 [Step 1033] loss_orig = 0.001309, loss_refine = 0.244002 [Step 1033] loss_orig = 0.001646, loss_refine = -0.722912 [Step 1033] loss_orig = 0.002309, loss_refine = -0.722029 [Step 1033] loss_orig = 0.002344, loss_refine = 2.174749 Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2103930: cache has only 0 modules 86%|████████▌ | 1034/1208 [13:14:55<2:26:28, 50.51s/it] {'loss': 0.0019, 'grad_norm': 3.491298830234443, 'learning_rate': 1.4403973509933772e-07, 'completion_length': 103.41666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.4166666666666665, 'reward_std': 0.3450327714284261, 'kl': 0.04107666015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 6.85} 86%|████████▌ | 1034/1208 [13:14:55<2:26:28, 50.51s/it]Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2104803: cache has only 0 modules Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2105676: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2106549: cache has only 0 modules [Step 1034] loss_orig = 0.000401, loss_refine = -0.350523[Step 1034] loss_orig = 0.000508, loss_refine = -0.351816[Step 1034] loss_orig = 0.001070, loss_refine = -0.351487 [Step 1034] loss_orig = 0.000717, loss_refine = -0.352210[Step 1034] loss_orig = 0.000587, loss_refine = -0.350826 [Step 1034] loss_orig = 0.000341, loss_refine = 2.475511 [Step 1034] loss_orig = 0.000296, loss_refine = -0.351299 [Step 1034] loss_orig = 0.000708, loss_refine = -0.350185 86%|████████▌ | 1035/1208 [13:15:44<2:24:16, 50.04s/it] {'loss': 0.0018, 'grad_norm': 6.979873581867798, 'learning_rate': 1.4321192052980132e-07, 'completion_length': 84.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.4082186420758565, 'kl': 0.024932861328125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 6.85} 86%|████████▌ | 1035/1208 [13:15:44<2:24:16, 50.04s/it]Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2107422: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2108295: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2109168: cache has only 0 modules [Step 1035] loss_orig = 0.001297, loss_refine = 0.001130[Step 1035] loss_orig = 0.002005, loss_refine = 0.002212 [Step 1035] loss_orig = 0.001783, loss_refine = 0.000905 [Step 1035] loss_orig = 0.001531, loss_refine = 0.001979[Step 1035] loss_orig = 0.003902, loss_refine = 0.001123 [Step 1035] loss_orig = 0.001195, loss_refine = 0.002419[Step 1035] loss_orig = 0.003341, loss_refine = 0.001611 [Step 1035] loss_orig = 0.005549, loss_refine = 0.003742 86%|████████▌ | 1036/1208 [13:16:41<2:29:43, 52.23s/it] {'loss': 0.0015, 'grad_norm': 0.4009565959648462, 'learning_rate': 1.423841059602649e-07, 'completion_length': 101.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.04705810546875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.86} 86%|████████▌ | 1036/1208 [13:16:41<2:29:43, 52.23s/it]Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2110041: cache has only 0 modules Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2110914: cache has only 0 modules 86%|████████▌ | 1037/1208 [13:17:22<2:18:55, 48.75s/it] {'loss': 0.0015, 'grad_norm': 11.871128887701104, 'learning_rate': 1.4155629139072846e-07, 'completion_length': 103.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.038330078125, 'clip_ratio': 0.0, 'epoch': 6.87} 86%|████████▌ | 1037/1208 [13:17:22<2:18:55, 48.75s/it]Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2111787: cache has only 0 modules Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2112660: cache has only 0 modules 86%|████████▌ | 1038/1208 [13:18:03<2:11:15, 46.33s/it] {'loss': 0.001, 'grad_norm': 8.926276308561542, 'learning_rate': 1.4072847682119203e-07, 'completion_length': 88.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02593994140625, 'clip_ratio': 0.0, 'epoch': 6.87} 86%|████████▌ | 1038/1208 [13:18:03<2:11:15, 46.33s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2113533: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2114406: cache has only 0 modules 86%|████████▌ | 1039/1208 [13:18:42<2:05:02, 44.39s/it] {'loss': 0.0023, 'grad_norm': 9.743614500602572, 'learning_rate': 1.3990066225165563e-07, 'completion_length': 95.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.05694580078125, 'clip_ratio': 0.0, 'epoch': 6.88} 86%|████████▌ | 1039/1208 [13:18:42<2:05:02, 44.39s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2115279: cache has only 0 modules Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2116152: cache has only 0 modules 86%|████████▌ | 1040/1208 [13:19:32<2:08:17, 45.82s/it] {'loss': 0.002, 'grad_norm': 3.5383578441206174, 'learning_rate': 1.390728476821192e-07, 'completion_length': 112.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0489501953125, 'clip_ratio': 0.0, 'epoch': 6.89} 86%|████████▌ | 1040/1208 [13:19:32<2:08:17, 45.82s/it]Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2117025: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2117898: cache has only 0 modules [Step 1040] loss_orig = 0.001404, loss_refine = -0.722789[Step 1040] loss_orig = 0.000634, loss_refine = -0.723591[Step 1040] loss_orig = 0.000324, loss_refine = -0.723846 [Step 1040] loss_orig = 0.001643, loss_refine = 1.208721 [Step 1040] loss_orig = 0.003878, loss_refine = 1.207806 [Step 1040] loss_orig = 0.000929, loss_refine = -0.723405 [Step 1040] loss_orig = 0.001156, loss_refine = -0.722946 [Step 1040] loss_orig = 0.000765, loss_refine = 1.208209 Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2118771: cache has only 0 modules 86%|████████▌ | 1041/1208 [13:20:35<2:22:08, 51.07s/it] {'loss': 0.001, 'grad_norm': 4.824088278970263, 'learning_rate': 1.382450331125828e-07, 'completion_length': 96.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5416666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.02886962890625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 6.89} 86%|████████▌ | 1041/1208 [13:20:35<2:22:08, 51.07s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2119644: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'invert the lens'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2120517: cache has only 0 modules [Step 1041] loss_orig = 0.000705, loss_refine = 0.002143 [Step 1041] loss_orig = 0.000842, loss_refine = 0.001181[Step 1041] loss_orig = 0.000859, loss_refine = 0.001868 [Step 1041] loss_orig = 0.001278, loss_refine = 0.001239[Step 1041] loss_orig = 0.001065, loss_refine = 0.002329 [Step 1041] loss_orig = 0.001389, loss_refine = 0.002043 [Step 1041] loss_orig = 0.000736, loss_refine = 0.001347 [Step 1041] loss_orig = 0.000761, loss_refine = 0.001608 Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2121390: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2122263: cache has only 0 modules [Step 1041] loss_orig = -0.352045, loss_refine = -0.722442[Step 1041] loss_orig = -0.351284, loss_refine = -0.720393 [Step 1041] loss_orig = -0.350253, loss_refine = 1.209333[Step 1041] loss_orig = -0.350429, loss_refine = 1.210160 [Step 1041] loss_orig = -0.352006, loss_refine = 1.209131 [Step 1041] loss_orig = -0.351495, loss_refine = -0.722073 [Step 1041] loss_orig = -0.351016, loss_refine = -0.722572 [Step 1041] loss_orig = 2.476358, loss_refine = -0.722632 86%|████████▋ | 1042/1208 [13:22:03<2:51:53, 62.13s/it] {'loss': 0.002, 'grad_norm': 3.76501934925311, 'learning_rate': 1.3741721854304636e-07, 'completion_length': 116.25, 'rewards/accuracy_reward_action': 0.96875, 'rewards/accuracy_reward_coord': 0.0, 'rewards/format_reward': 0.96875, 'reward': 2.09375, 'reward_std': 0.3061639815568924, 'kl': 0.039794921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.3125, 'epoch': 6.9} 86%|████████▋ | 1042/1208 [13:22:03<2:51:53, 62.13s/it]Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2123136: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2124009: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display more functions'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2124882: cache has only 0 modules [Step 1042] loss_orig = 0.001069, loss_refine = -0.351503[Step 1042] loss_orig = 0.001681, loss_refine = -0.350539[Step 1042] loss_orig = 0.004009, loss_refine = -0.350161 [Step 1042] loss_orig = 0.001463, loss_refine = -0.352416 [Step 1042] loss_orig = 0.001416, loss_refine = -0.351513[Step 1042] loss_orig = 0.001457, loss_refine = 2.475269 [Step 1042] loss_orig = 0.001338, loss_refine = -0.352004 [Step 1042] loss_orig = 0.001718, loss_refine = -0.349956 86%|████████▋ | 1043/1208 [13:22:57<2:44:02, 59.65s/it] {'loss': 0.0032, 'grad_norm': 18.200454587995296, 'learning_rate': 1.3658940397350993e-07, 'completion_length': 90.70833333333333, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.5833333333333334, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0740966796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.91} 86%|████████▋ | 1043/1208 [13:22:57<2:44:02, 59.65s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2125755: cache has only 0 modules Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2126628: cache has only 0 modules 86%|████████▋ | 1044/1208 [13:23:36<2:26:37, 53.65s/it] {'loss': 0.002, 'grad_norm': 11.930529127149333, 'learning_rate': 1.357615894039735e-07, 'completion_length': 95.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0509033203125, 'clip_ratio': 0.0, 'epoch': 6.91} 86%|████████▋ | 1044/1208 [13:23:36<2:26:37, 53.65s/it]Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2127501: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2128374: cache has only 0 modules 87%|████████▋ | 1045/1208 [13:24:15<2:13:25, 49.11s/it] {'loss': 0.0019, 'grad_norm': 6.317086223484083, 'learning_rate': 1.3493377483443707e-07, 'completion_length': 95.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.3535533845424652, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'epoch': 6.92} 87%|████████▋ | 1045/1208 [13:24:15<2:13:25, 49.11s/it]Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2129247: cache has only 0 modules Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2130120: cache has only 0 modules 87%|████████▋ | 1046/1208 [13:25:02<2:11:02, 48.54s/it] {'loss': 0.0017, 'grad_norm': 0.4449070767769168, 'learning_rate': 1.3410596026490064e-07, 'completion_length': 97.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0421142578125, 'clip_ratio': 0.0, 'epoch': 6.93} 87%|████████▋ | 1046/1208 [13:25:02<2:11:02, 48.54s/it]Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2130993: cache has only 0 modules Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2131866: cache has only 0 modules 87%|████████▋ | 1047/1208 [13:25:41<2:02:27, 45.64s/it] {'loss': 0.0013, 'grad_norm': 20.166733326582758, 'learning_rate': 1.3327814569536421e-07, 'completion_length': 93.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.0318603515625, 'clip_ratio': 0.0, 'epoch': 6.93} 87%|████████▋ | 1047/1208 [13:25:41<2:02:27, 45.64s/it]Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2132739: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [845, 95]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2133612: cache has only 0 modules [Step 1047] loss_orig = 0.001283, loss_refine = 0.001325[Step 1047] loss_orig = 0.000668, loss_refine = 0.000828 [Step 1047] loss_orig = 0.001484, loss_refine = 0.001058[Step 1047] loss_orig = 0.002069, loss_refine = 0.001529 [Step 1047] loss_orig = 0.002493, loss_refine = 0.001013 [Step 1047] loss_orig = 0.001354, loss_refine = 0.001363 [Step 1047] loss_orig = 0.004218, loss_refine = 0.001005[Step 1047] loss_orig = 0.001769, loss_refine = 0.001491 Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2134485: cache has only 0 modules 87%|████████▋ | 1048/1208 [13:26:38<2:10:46, 49.04s/it] {'loss': 0.001, 'grad_norm': 0.24235332531632311, 'learning_rate': 1.324503311258278e-07, 'completion_length': 107.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.0340576171875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 6.94} 87%|████████▋ | 1048/1208 [13:26:38<2:10:46, 49.04s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2135358: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [453, 117]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2136231: cache has only 0 modules [Step 1048] loss_orig = 0.001929, loss_refine = 0.541288[Step 1048] loss_orig = 0.001969, loss_refine = -1.618281[Step 1048] loss_orig = 0.001592, loss_refine = 0.541297[Step 1048] loss_orig = 0.001189, loss_refine = -1.616462[Step 1048] loss_orig = 0.001562, loss_refine = 0.541912 [Step 1048] loss_orig = 0.002974, loss_refine = 0.541195[Step 1048] loss_orig = 0.001041, loss_refine = 0.540942[Step 1048] loss_orig = 0.001454, loss_refine = 0.543387 Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2137104: cache has only 0 modules 87%|████████▋ | 1049/1208 [13:27:32<2:13:49, 50.50s/it] {'loss': 0.0015, 'grad_norm': 8.142911466334853, 'learning_rate': 1.3162251655629138e-07, 'completion_length': 108.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.27215448021888733, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 6.95} 87%|████████▋ | 1049/1208 [13:27:32<2:13:49, 50.50s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2137977: cache has only 0 modules Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2138850: cache has only 0 modules 87%|████████▋ | 1050/1208 [13:28:14<2:06:39, 48.10s/it] {'loss': 0.0009, 'grad_norm': 0.31655831363155934, 'learning_rate': 1.3079470198675495e-07, 'completion_length': 102.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02166748046875, 'clip_ratio': 0.0, 'epoch': 6.95} 87%|████████▋ | 1050/1208 [13:28:14<2:06:39, 48.10s/it]Start loss calc for inst: click the UI element Apple /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2139723: cache has only 0 modules Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2140596: cache has only 0 modules 87%|████████▋ | 1051/1208 [13:29:16<2:16:19, 52.10s/it] {'loss': 0.0012, 'grad_norm': 5.322771903348001, 'learning_rate': 1.2996688741721855e-07, 'completion_length': 94.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03125, 'clip_ratio': 0.0, 'epoch': 6.96} 87%|████████▋ | 1051/1208 [13:29:16<2:16:19, 52.10s/it]Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2141469: cache has only 0 modules Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2142342: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2143215: cache has only 0 modules [Step 1051] loss_orig = 0.001571, loss_refine = 0.001477[Step 1051] loss_orig = 0.001386, loss_refine = 0.000577[Step 1051] loss_orig = 0.002739, loss_refine = 0.000808[Step 1051] loss_orig = 0.000628, loss_refine = 0.001043 [Step 1051] loss_orig = 0.000821, loss_refine = 0.001082[Step 1051] loss_orig = 0.000876, loss_refine = 0.001109 [Step 1051] loss_orig = 0.000764, loss_refine = 0.001349 [Step 1051] loss_orig = 0.001604, loss_refine = 0.001922 87%|████████▋ | 1052/1208 [13:30:12<2:18:53, 53.42s/it] {'loss': 0.0026, 'grad_norm': 5.762207249986206, 'learning_rate': 1.2913907284768212e-07, 'completion_length': 95.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.25, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.15430335203806558, 'kl': 0.0675048828125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 6.97} 87%|████████▋ | 1052/1208 [13:30:12<2:18:53, 53.42s/it]Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2144088: cache has only 0 modules Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2144961: cache has only 0 modules 87%|████████▋ | 1053/1208 [13:30:59<2:12:56, 51.46s/it] {'loss': 0.001, 'grad_norm': 4.179182234944268, 'learning_rate': 1.283112582781457e-07, 'completion_length': 109.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 0.9375, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02484130859375, 'clip_ratio': 0.0, 'epoch': 6.97} 87%|████████▋ | 1053/1208 [13:30:59<2:12:56, 51.46s/it]Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2145834: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2146707: cache has only 0 modules 87%|████████▋ | 1054/1208 [13:31:32<1:58:00, 45.98s/it] {'loss': 0.0009, 'grad_norm': 0.2824041431660922, 'learning_rate': 1.2748344370860928e-07, 'completion_length': 83.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.021270751953125, 'clip_ratio': 0.0, 'epoch': 6.98} 87%|████████▋ | 1054/1208 [13:31:32<1:58:00, 45.98s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2147580: cache has only 0 modules Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2148453: cache has only 0 modules 87%|████████▋ | 1055/1208 [13:32:12<1:52:19, 44.05s/it] {'loss': 0.0033, 'grad_norm': 8.348734586616736, 'learning_rate': 1.2665562913907285e-07, 'completion_length': 95.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.49022960662841797, 'kl': 0.0819091796875, 'clip_ratio': 0.0, 'epoch': 6.99} 87%|████████▋ | 1055/1208 [13:32:12<1:52:19, 44.05s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2149326: cache has only 0 modules Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2150199: cache has only 0 modules 87%|████████▋ | 1056/1208 [13:32:48<1:45:54, 41.80s/it] {'loss': 0.0021, 'grad_norm': 5.428769551765114, 'learning_rate': 1.2582781456953642e-07, 'completion_length': 100.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.0537109375, 'clip_ratio': 0.0, 'epoch': 6.99} 87%|████████▋ | 1056/1208 [13:32:48<1:45:54, 41.80s/it]Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2151072: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2151945: cache has only 0 modules 88%|████████▊ | 1057/1208 [13:33:22<1:38:51, 39.28s/it] {'loss': 0.0008, 'grad_norm': 0.6416856183517566, 'learning_rate': 1.25e-07, 'completion_length': 74.41667175292969, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.01953125, 'clip_ratio': 0.0, 'epoch': 7.0} 88%|████████▊ | 1057/1208 [13:33:22<1:38:51, 39.28s/it]Start loss calc for inst: click the UI element Subscript Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2152818: cache has only 0 modules Start loss calc for inst: choose watercolor brush style Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2153691: cache has only 0 modules 88%|████████▊ | 1058/1208 [13:34:11<1:45:36, 42.24s/it] {'loss': 0.0013, 'grad_norm': 6.8457905583250644, 'learning_rate': 1.2417218543046356e-07, 'completion_length': 115.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3125, 'rewards/format_reward': 1.0, 'reward': 2.3125, 'reward_std': 0.49022960662841797, 'kl': 0.0333251953125, 'clip_ratio': 0.0, 'epoch': 7.01} 88%|████████▊ | 1058/1208 [13:34:11<1:45:36, 42.24s/it]Start loss calc for inst: click the UI element From Current Slide... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2154564: cache has only 0 modules Start loss calc for inst: click the UI element Consumer Health Data Privacy Policy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2155437: cache has only 0 modules 88%|████████▊ | 1059/1208 [13:34:50<1:42:47, 41.39s/it] {'loss': 0.0009, 'grad_norm': 0.30723113084441, 'learning_rate': 1.2334437086092716e-07, 'completion_length': 93.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.023040771484375, 'clip_ratio': 0.0, 'epoch': 7.01} 88%|████████▊ | 1059/1208 [13:34:50<1:42:47, 41.39s/it]Start loss calc for inst: flod this content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2156310: cache has only 0 modules Start loss calc for inst: click the UI element Amazon Music Stream millions of songs Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2157183: cache has only 0 modules 88%|████████▊ | 1060/1208 [13:35:33<1:43:16, 41.87s/it] {'loss': 0.0014, 'grad_norm': 4.896176981148267, 'learning_rate': 1.2251655629139073e-07, 'completion_length': 89.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.033935546875, 'clip_ratio': 0.0, 'epoch': 7.02} 88%|████████▊ | 1060/1208 [13:35:33<1:43:16, 41.87s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2158056: cache has only 0 modules Start loss calc for inst: open files in ipad Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2158929: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open files in ipad'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2159802: cache has only 0 modules [Step 1060] loss_orig = 0.001657, loss_refine = 0.542281 [Step 1060] loss_orig = 0.001444, loss_refine = -1.618148[Step 1060] loss_orig = 0.003423, loss_refine = 0.541743 [Step 1060] loss_orig = 0.001020, loss_refine = -1.618474[Step 1060] loss_orig = 0.000826, loss_refine = 0.542711 [Step 1060] loss_orig = 0.000607, loss_refine = 0.540782 [Step 1060] loss_orig = 0.000436, loss_refine = 0.540912 [Step 1060] loss_orig = 0.001633, loss_refine = 0.540417 88%|████████▊ | 1061/1208 [13:36:33<1:55:59, 47.34s/it] {'loss': 0.0023, 'grad_norm': 9.435680406466393, 'learning_rate': 1.216887417218543e-07, 'completion_length': 89.91666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4166666666666667, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.15430335203806558, 'kl': 0.0555419921875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.03} 88%|████████▊ | 1061/1208 [13:36:33<1:55:59, 47.34s/it]Start loss calc for inst: join a twitch server Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2160675: cache has only 0 modules Start loss calc for inst: click the UI element Collaborate with groups Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2161548: cache has only 0 modules 88%|████████▊ | 1062/1208 [13:37:09<1:46:46, 43.88s/it] {'loss': 0.0007, 'grad_norm': 0.12621692950736443, 'learning_rate': 1.2086092715231787e-07, 'completion_length': 87.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.018798828125, 'clip_ratio': 0.0, 'epoch': 7.03} 88%|████████▊ | 1062/1208 [13:37:09<1:46:46, 43.88s/it]Start loss calc for inst: click the UI element Google Images Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2162421: cache has only 0 modules Start loss calc for inst: raise air conditioner temperature Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2163294: cache has only 0 modules 88%|████████▊ | 1063/1208 [13:37:41<1:37:02, 40.16s/it] {'loss': 0.0023, 'grad_norm': 9.920981843486139, 'learning_rate': 1.2003311258278144e-07, 'completion_length': 85.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0584716796875, 'clip_ratio': 0.0, 'epoch': 7.04} 88%|████████▊ | 1063/1208 [13:37:41<1:37:02, 40.16s/it]Start loss calc for inst: click the UI element Accessibility Menu Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2164167: cache has only 0 modules Start loss calc for inst: click the UI element Less Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2165040: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Less'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2165913: cache has only 0 modules [Step 1063] loss_orig = 0.001989, loss_refine = 0.727384[Step 1063] loss_orig = 0.001472, loss_refine = -1.204895[Step 1063] loss_orig = 0.001787, loss_refine = 0.728093[Step 1063] loss_orig = 0.001921, loss_refine = -1.202454 [Step 1063] loss_orig = 0.001847, loss_refine = 0.727139[Step 1063] loss_orig = 0.002230, loss_refine = 0.728449 [Step 1063] loss_orig = 0.001417, loss_refine = 0.726313 [Step 1063] loss_orig = 0.001302, loss_refine = -1.205725 88%|████████▊ | 1064/1208 [13:38:47<1:55:26, 48.10s/it] {'loss': 0.002, 'grad_norm': 5.740522163436652, 'learning_rate': 1.1920529801324502e-07, 'completion_length': 109.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.03509521484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.05} 88%|████████▊ | 1064/1208 [13:38:47<1:55:26, 48.10s/it]Start loss calc for inst: click the UI element Google Chrome Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2166786: cache has only 0 modules Start loss calc for inst: use airplay Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2167659: cache has only 0 modules 88%|████████▊ | 1065/1208 [13:39:26<1:48:06, 45.36s/it] {'loss': 0.0026, 'grad_norm': 5.134269330829767, 'learning_rate': 1.183774834437086e-07, 'completion_length': 91.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.06524658203125, 'clip_ratio': 0.0, 'epoch': 7.05} 88%|████████▊ | 1065/1208 [13:39:26<1:48:06, 45.36s/it]Start loss calc for inst: cancel subscription Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2168532: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: RightScrollButton Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2169405: cache has only 0 modules 88%|████████▊ | 1066/1208 [13:40:13<1:48:25, 45.81s/it] {'loss': 0.0019, 'grad_norm': 0.4640429451308686, 'learning_rate': 1.1754966887417218e-07, 'completion_length': 119.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04736328125, 'clip_ratio': 0.0, 'epoch': 7.06} 88%|████████▊ | 1066/1208 [13:40:13<1:48:25, 45.81s/it]Start loss calc for inst: click the UI element Sheet1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2170278: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2171151: cache has only 0 modules 88%|████████▊ | 1067/1208 [13:40:52<1:42:38, 43.67s/it] {'loss': 0.0009, 'grad_norm': 7.601957468715182, 'learning_rate': 1.1672185430463576e-07, 'completion_length': 91.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0224609375, 'clip_ratio': 0.0, 'epoch': 7.07} 88%|████████▊ | 1067/1208 [13:40:52<1:42:38, 43.67s/it]Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2172024: cache has only 0 modules Start loss calc for inst: download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2172897: cache has only 0 modules 88%|████████▊ | 1068/1208 [13:41:28<1:36:36, 41.40s/it] {'loss': 0.0015, 'grad_norm': 0.28316684238123613, 'learning_rate': 1.1589403973509933e-07, 'completion_length': 91.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03802490234375, 'clip_ratio': 0.0, 'epoch': 7.07} 88%|████████▊ | 1068/1208 [13:41:28<1:36:36, 41.40s/it]Start loss calc for inst: remove the camera from the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2173770: cache has only 0 modules Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2174643: cache has only 0 modules 88%|████████▊ | 1069/1208 [13:42:04<1:32:26, 39.91s/it] {'loss': 0.0022, 'grad_norm': 4.355117497875847, 'learning_rate': 1.1506622516556291e-07, 'completion_length': 82.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0543212890625, 'clip_ratio': 0.0, 'epoch': 7.08} 88%|████████▊ | 1069/1208 [13:42:04<1:32:26, 39.91s/it]Start loss calc for inst: click the UI element Use GitLab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2175516: cache has only 0 modules Start loss calc for inst: click the UI element Simplified Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2176389: cache has only 0 modules 89%|████████▊ | 1070/1208 [13:42:45<1:32:00, 40.01s/it] {'loss': 0.001, 'grad_norm': 0.23094928369895637, 'learning_rate': 1.1423841059602648e-07, 'completion_length': 91.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0238037109375, 'clip_ratio': 0.0, 'epoch': 7.09} 89%|████████▊ | 1070/1208 [13:42:45<1:32:00, 40.01s/it]Start loss calc for inst: click the UI element Conciseness, 0 issues. Press space or enter to review items. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2177262: cache has only 0 modules Start loss calc for inst: click the UI element SPX +0.16% S&P 500 Index 5,625.80 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2178135: cache has only 0 modules 89%|████████▊ | 1071/1208 [13:43:25<1:31:42, 40.17s/it] {'loss': 0.0009, 'grad_norm': 6.026174889478992, 'learning_rate': 1.1341059602649005e-07, 'completion_length': 115.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02301025390625, 'clip_ratio': 0.0, 'epoch': 7.09} 89%|████████▊ | 1071/1208 [13:43:25<1:31:42, 40.17s/it]Start loss calc for inst: click the UI element Guides, selected Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2179008: cache has only 0 modules Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2179881: cache has only 0 modules 89%|████████▊ | 1072/1208 [13:44:00<1:27:29, 38.60s/it] {'loss': 0.0011, 'grad_norm': 0.1252673024011209, 'learning_rate': 1.1258278145695364e-07, 'completion_length': 84.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02630615234375, 'clip_ratio': 0.0, 'epoch': 7.1} 89%|████████▊ | 1072/1208 [13:44:00<1:27:29, 38.60s/it]Start loss calc for inst: click the UI element Tray Input Indicator - Chinese (Simplified, China) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2180754: cache has only 0 modules Start loss calc for inst: click the UI element IMAGES Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2181627: cache has only 0 modules 89%|████████▉ | 1073/1208 [13:44:41<1:28:09, 39.18s/it] {'loss': 0.0017, 'grad_norm': 9.437015028761106, 'learning_rate': 1.1175496688741722e-07, 'completion_length': 96.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.04217529296875, 'clip_ratio': 0.0, 'epoch': 7.11} 89%|████████▉ | 1073/1208 [13:44:41<1:28:09, 39.18s/it]Start loss calc for inst: click the UI element Evan You Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2182500: cache has only 0 modules Start loss calc for inst: show news Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2183373: cache has only 0 modules 89%|████████▉ | 1074/1208 [13:45:24<1:30:25, 40.49s/it] {'loss': 0.0011, 'grad_norm': 6.392932191844063, 'learning_rate': 1.1092715231788079e-07, 'completion_length': 95.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0277099609375, 'clip_ratio': 0.0, 'epoch': 7.11} 89%|████████▉ | 1074/1208 [13:45:24<1:30:25, 40.49s/it]Start loss calc for inst: click the UI element Privacy Checkup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2184246: cache has only 0 modules Start loss calc for inst: click the UI element Split screen Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2185119: cache has only 0 modules 89%|████████▉ | 1075/1208 [13:46:03<1:28:45, 40.04s/it] {'loss': 0.0029, 'grad_norm': 4.868232969200126, 'learning_rate': 1.1009933774834436e-07, 'completion_length': 97.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.072509765625, 'clip_ratio': 0.0, 'epoch': 7.12} 89%|████████▉ | 1075/1208 [13:46:03<1:28:45, 40.04s/it]Start loss calc for inst: locked rotation Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2185992: cache has only 0 modules Start loss calc for inst: check out jony j's album Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2186865: cache has only 0 modules 89%|████████▉ | 1076/1208 [13:46:44<1:28:41, 40.32s/it] {'loss': 0.0014, 'grad_norm': 5.562628885373598, 'learning_rate': 1.0927152317880794e-07, 'completion_length': 93.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.03466796875, 'clip_ratio': 0.0, 'epoch': 7.13} 89%|████████▉ | 1076/1208 [13:46:44<1:28:41, 40.32s/it]Start loss calc for inst: click the UI element Table Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2187738: cache has only 0 modules Start loss calc for inst: click the UI element Address and search bar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2188611: cache has only 0 modules 89%|████████▉ | 1077/1208 [13:47:32<1:33:02, 42.62s/it] {'loss': 0.0014, 'grad_norm': 13.748971280537017, 'learning_rate': 1.0844370860927151e-07, 'completion_length': 107.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.035888671875, 'clip_ratio': 0.0, 'epoch': 7.13} 89%|████████▉ | 1077/1208 [13:47:32<1:33:02, 42.62s/it]Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2189484: cache has only 0 modules Start loss calc for inst: click the UI element Warsaw Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2190357: cache has only 0 modules 89%|████████▉ | 1078/1208 [13:48:18<1:34:40, 43.70s/it] {'loss': 0.0013, 'grad_norm': 0.46619085373804586, 'learning_rate': 1.0761589403973508e-07, 'completion_length': 103.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03131103515625, 'clip_ratio': 0.0, 'epoch': 7.14} 89%|████████▉ | 1078/1208 [13:48:18<1:34:40, 43.70s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2191230: cache has only 0 modules Start loss calc for inst: adjust the voice Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2192103: cache has only 0 modules 89%|████████▉ | 1079/1208 [13:48:53<1:27:58, 40.92s/it] {'loss': 0.0019, 'grad_norm': 0.5893349639974662, 'learning_rate': 1.0678807947019868e-07, 'completion_length': 88.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04852294921875, 'clip_ratio': 0.0, 'epoch': 7.15} 89%|████████▉ | 1079/1208 [13:48:53<1:27:58, 40.92s/it]Start loss calc for inst: click the UI element https://lexfridman.com/sponsors/ep438-sb Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2192976: cache has only 0 modules Start loss calc for inst: click the UI element Dale O'Donnell Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2193849: cache has only 0 modules 89%|████████▉ | 1080/1208 [13:49:37<1:29:03, 41.75s/it] {'loss': 0.0022, 'grad_norm': 8.26662881922156, 'learning_rate': 1.0596026490066225e-07, 'completion_length': 110.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 1.0, 'reward': 2.6875, 'reward_std': 0.2587745785713196, 'kl': 0.05609130859375, 'clip_ratio': 0.0, 'epoch': 7.15} 89%|████████▉ | 1080/1208 [13:49:37<1:29:03, 41.75s/it]Start loss calc for inst: click the UI element 10Ft Extension Cord with Multiple Outlets, Flat Plug Power Strip Surge Protector with 10 Ft Long Cord, 6 Outlet 3 USB Port... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2194722: cache has only 0 modules Start loss calc for inst: click the UI element Color Management Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2195595: cache has only 0 modules 89%|████████▉ | 1081/1208 [13:50:13<1:25:17, 40.29s/it] {'loss': 0.0008, 'grad_norm': 0.23381878671375722, 'learning_rate': 1.0513245033112582e-07, 'completion_length': 101.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02056884765625, 'clip_ratio': 0.0, 'epoch': 7.16} 89%|████████▉ | 1081/1208 [13:50:13<1:25:17, 40.29s/it]Start loss calc for inst: click the UI element Slide Show Next On Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2196468: cache has only 0 modules Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2197341: cache has only 0 modules 90%|████████▉ | 1082/1208 [13:51:04<1:31:01, 43.34s/it] {'loss': 0.0014, 'grad_norm': 13.417131756695948, 'learning_rate': 1.043046357615894e-07, 'completion_length': 110.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03497314453125, 'clip_ratio': 0.0, 'epoch': 7.17} 90%|████████▉ | 1082/1208 [13:51:04<1:31:01, 43.34s/it]Start loss calc for inst: remove chrome from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2198214: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'remove chrome from the desktop'. Your previous answer was [{'action': 'click', 'coordinate': [1014, 961]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2199087: cache has only 0 modules [Step 1082] loss_orig = 0.001028, loss_refine = -0.934076[Step 1082] loss_orig = 0.000722, loss_refine = -0.933982 [Step 1082] loss_orig = 0.001195, loss_refine = 0.936745[Step 1082] loss_orig = 0.001571, loss_refine = 0.936897 [Step 1082] loss_orig = 0.001185, loss_refine = 0.936659 [Step 1082] loss_orig = 0.002146, loss_refine = 0.937688 [Step 1082] loss_orig = 0.001648, loss_refine = -0.934001 [Step 1082] loss_orig = 0.001994, loss_refine = -0.933618 Start loss calc for inst: display user agreement Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2199960: cache has only 0 modules 90%|████████▉ | 1083/1208 [13:51:53<1:34:04, 45.16s/it] {'loss': 0.0025, 'grad_norm': 8.846606173969246, 'learning_rate': 1.0347682119205297e-07, 'completion_length': 77.75, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.41387641429901123, 'kl': 0.0616455078125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 7.17} 90%|████████▉ | 1083/1208 [13:51:53<1:34:04, 45.16s/it]Start loss calc for inst: display ip address Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2200833: cache has only 0 modules Start loss calc for inst: click the UI element Visual Studio Code - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2201706: cache has only 0 modules 90%|████████▉ | 1084/1208 [13:52:37<1:32:10, 44.60s/it] {'loss': 0.0056, 'grad_norm': 5.004285395609068, 'learning_rate': 1.0264900662251654e-07, 'completion_length': 109.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.1396484375, 'clip_ratio': 0.0, 'epoch': 7.18} 90%|████████▉ | 1084/1208 [13:52:37<1:32:10, 44.60s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2202579: cache has only 0 modules Start loss calc for inst: remove maps from the desktop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2203452: cache has only 0 modules 90%|████████▉ | 1085/1208 [13:53:19<1:30:00, 43.91s/it] {'loss': 0.0035, 'grad_norm': 8.648055088347792, 'learning_rate': 1.0182119205298013e-07, 'completion_length': 108.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5, 'rewards/format_reward': 0.9375, 'reward': 2.375, 'reward_std': 0.7104638516902924, 'kl': 0.087158203125, 'clip_ratio': 0.0, 'epoch': 7.19} 90%|████████▉ | 1085/1208 [13:53:19<1:30:00, 43.91s/it]Start loss calc for inst: click the UI element Gilma and Hector both pose tropical trouble for Hawaii Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2204325: cache has only 0 modules Start loss calc for inst: add new email account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2205198: cache has only 0 modules 90%|████████▉ | 1086/1208 [13:53:57<1:25:30, 42.05s/it] {'loss': 0.0019, 'grad_norm': 6.344711264148428, 'learning_rate': 1.0099337748344371e-07, 'completion_length': 94.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.04833984375, 'clip_ratio': 0.0, 'epoch': 7.19} 90%|████████▉ | 1086/1208 [13:53:57<1:25:30, 42.05s/it]Start loss calc for inst: click the UI element New Photo Album... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2206071: cache has only 0 modules Start loss calc for inst: click the UI element Disable Linked Styles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2206944: cache has only 0 modules 90%|████████▉ | 1087/1208 [13:54:36<1:23:17, 41.30s/it] {'loss': 0.0014, 'grad_norm': 4.445073896091624, 'learning_rate': 1.0016556291390728e-07, 'completion_length': 97.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.03582763671875, 'clip_ratio': 0.0, 'epoch': 7.2} 90%|████████▉ | 1087/1208 [13:54:36<1:23:17, 41.30s/it]Start loss calc for inst: click the UI element Currencies - Google Finance Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2207817: cache has only 0 modules Start loss calc for inst: play video Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2208690: cache has only 0 modules 90%|█████████ | 1088/1208 [13:55:20<1:24:18, 42.16s/it] {'loss': 0.001, 'grad_norm': 0.3327777411535214, 'learning_rate': 9.933774834437085e-08, 'completion_length': 97.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.025146484375, 'clip_ratio': 0.0, 'epoch': 7.21} 90%|█████████ | 1088/1208 [13:55:20<1:24:18, 42.16s/it]Start loss calc for inst: display noticfications Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2209563: cache has only 0 modules Start loss calc for inst: view world clock Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2210436: cache has only 0 modules 90%|█████████ | 1089/1208 [13:56:05<1:24:55, 42.82s/it] {'loss': 0.0013, 'grad_norm': 0.312642043613064, 'learning_rate': 9.850993377483443e-08, 'completion_length': 90.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0313720703125, 'clip_ratio': 0.0, 'epoch': 7.21} 90%|█████████ | 1089/1208 [13:56:05<1:24:55, 42.82s/it]Start loss calc for inst: view exercise log on map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2211309: cache has only 0 modules Start loss calc for inst: forwarding Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2212182: cache has only 0 modules 90%|█████████ | 1090/1208 [13:56:49<1:25:21, 43.40s/it] {'loss': 0.0048, 'grad_norm': 4.326227493329708, 'learning_rate': 9.7682119205298e-08, 'completion_length': 99.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.119873046875, 'clip_ratio': 0.0, 'epoch': 7.22} 90%|█████████ | 1090/1208 [13:56:49<1:25:21, 43.40s/it]Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2213055: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: BadgeAnchorLargeTicker'. Your previous answer was [{'action': 'click', 'coordinate': [103, 1555]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2213928: cache has only 0 modules [Step 1090] loss_orig = -0.658053, loss_refine = 0.003285 [Step 1090] loss_orig = 0.666306, loss_refine = 0.001232[Step 1090] loss_orig = -0.659377, loss_refine = 0.001890 [Step 1090] loss_orig = -0.660285, loss_refine = 0.001296 [Step 1090] loss_orig = 1.986348, loss_refine = 0.001202 [Step 1090] loss_orig = -0.658539, loss_refine = 0.001275 [Step 1090] loss_orig = -0.659498, loss_refine = 0.001337 [Step 1090] loss_orig = 0.662491, loss_refine = 0.001237 Start loss calc for inst: adjust end time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2214801: cache has only 0 modules 90%|█████████ | 1091/1208 [13:58:01<1:41:11, 51.90s/it] {'loss': 0.0016, 'grad_norm': 0.22299318640330795, 'learning_rate': 9.685430463576157e-08, 'completion_length': 116.45833333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.1666666666666665, 'reward_std': 0.2519763112068176, 'kl': 0.05029296875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 7.23} 90%|█████████ | 1091/1208 [13:58:01<1:41:11, 51.90s/it]Start loss calc for inst: click the UI element Map Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2215674: cache has only 0 modules Start loss calc for inst: click the UI element Group... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2216547: cache has only 0 modules 90%|█████████ | 1092/1208 [13:58:34<1:29:11, 46.14s/it] {'loss': 0.0009, 'grad_norm': 0.4768169991439571, 'learning_rate': 9.602649006622517e-08, 'completion_length': 82.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.022705078125, 'clip_ratio': 0.0, 'epoch': 7.23} 90%|█████████ | 1092/1208 [13:58:34<1:29:11, 46.14s/it]Start loss calc for inst: click the UI element Blog Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2217420: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2218293: cache has only 0 modules 90%|█████████ | 1093/1208 [13:59:10<1:22:31, 43.06s/it] {'loss': 0.0008, 'grad_norm': 0.11997986143156845, 'learning_rate': 9.519867549668874e-08, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0203857421875, 'clip_ratio': 0.0, 'epoch': 7.24} 90%|█████████ | 1093/1208 [13:59:10<1:22:31, 43.06s/it]Start loss calc for inst: click the UI element Accept Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2219166: cache has only 0 modules Start loss calc for inst: show policy agreement Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2220039: cache has only 0 modules 91%|█████████ | 1094/1208 [13:59:51<1:21:04, 42.67s/it] {'loss': 0.0009, 'grad_norm': 0.1604880662295189, 'learning_rate': 9.437086092715231e-08, 'completion_length': 97.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.021240234375, 'clip_ratio': 0.0, 'epoch': 7.25} 91%|█████████ | 1094/1208 [13:59:51<1:21:04, 42.67s/it]Start loss calc for inst: click the UI element Gente TMRG Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2220912: cache has only 0 modules Start loss calc for inst: more settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2221785: cache has only 0 modules 91%|█████████ | 1095/1208 [14:00:27<1:16:33, 40.65s/it] {'loss': 0.0018, 'grad_norm': 17.622623676547665, 'learning_rate': 9.35430463576159e-08, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4629100561141968, 'kl': 0.04534912109375, 'clip_ratio': 0.0, 'epoch': 7.25} 91%|█████████ | 1095/1208 [14:00:27<1:16:33, 40.65s/it]Start loss calc for inst: click the UI element Chrome Web Store Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2222658: cache has only 0 modules Start loss calc for inst: click the UI element Height Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2223531: cache has only 0 modules 91%|█████████ | 1096/1208 [14:01:05<1:14:23, 39.85s/it] {'loss': 0.0017, 'grad_norm': 8.018789871308629, 'learning_rate': 9.271523178807946e-08, 'completion_length': 86.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.04180908203125, 'clip_ratio': 0.0, 'epoch': 7.26} 91%|█████████ | 1096/1208 [14:01:05<1:14:23, 39.85s/it]Start loss calc for inst: click the UI element Channel watermark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2224404: cache has only 0 modules Start loss calc for inst: close the tab with the apple official website Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2225277: cache has only 0 modules 91%|█████████ | 1097/1208 [14:01:50<1:16:07, 41.15s/it] {'loss': 0.0018, 'grad_norm': 7.861655423725328, 'learning_rate': 9.188741721854304e-08, 'completion_length': 100.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.4375, 'reward_std': 0.408231720328331, 'kl': 0.0455322265625, 'clip_ratio': 0.0, 'epoch': 7.26} 91%|█████████ | 1097/1208 [14:01:50<1:16:07, 41.15s/it]Start loss calc for inst: click the UI element MAPS Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2226150: cache has only 0 modules Start loss calc for inst: edit the overlay of this page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2227023: cache has only 0 modules 91%|█████████ | 1098/1208 [14:02:27<1:13:28, 40.08s/it] {'loss': 0.0014, 'grad_norm': 5.136223290834905, 'learning_rate': 9.10596026490066e-08, 'completion_length': 93.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'epoch': 7.27} 91%|█████████ | 1098/1208 [14:02:27<1:13:28, 40.08s/it]Start loss calc for inst: click the UI element Czech (detected) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2227896: cache has only 0 modules Start loss calc for inst: add a emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2228769: cache has only 0 modules 91%|█████████ | 1099/1208 [14:03:09<1:13:50, 40.64s/it] {'loss': 0.0049, 'grad_norm': 7.2048224299267325, 'learning_rate': 9.02317880794702e-08, 'completion_length': 110.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.12286376953125, 'clip_ratio': 0.0, 'epoch': 7.28} 91%|█████████ | 1099/1208 [14:03:09<1:13:50, 40.64s/it]Start loss calc for inst: click the UI element Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2229642: cache has only 0 modules Start loss calc for inst: click the UI element Track Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2230515: cache has only 0 modules 91%|█████████ | 1100/1208 [14:03:56<1:16:38, 42.58s/it] {'loss': 0.0018, 'grad_norm': 10.743167567362315, 'learning_rate': 8.940397350993377e-08, 'completion_length': 115.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0445556640625, 'clip_ratio': 0.0, 'epoch': 7.28} 91%|█████████ | 1100/1208 [14:03:56<1:16:38, 42.58s/it]Start loss calc for inst: click the UI element AutomationID: Icons_Abacus_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2231388: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2232261: cache has only 0 modules 91%|█████████ | 1101/1208 [14:04:35<1:13:39, 41.30s/it] {'loss': 0.0034, 'grad_norm': 10.161350522103671, 'learning_rate': 8.857615894039734e-08, 'completion_length': 97.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0855712890625, 'clip_ratio': 0.0, 'epoch': 7.29} 91%|█████████ | 1101/1208 [14:04:35<1:13:39, 41.30s/it]Start loss calc for inst: click the UI element Explore poe Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2233134: cache has only 0 modules Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2234007: cache has only 0 modules 91%|█████████ | 1102/1208 [14:05:13<1:11:14, 40.32s/it] {'loss': 0.0006, 'grad_norm': 0.16695506699526794, 'learning_rate': 8.774834437086093e-08, 'completion_length': 86.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.015350341796875, 'clip_ratio': 0.0, 'epoch': 7.3} 91%|█████████ | 1102/1208 [14:05:13<1:11:14, 40.32s/it]Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2234880: cache has only 0 modules Start loss calc for inst: click the UI element Ad info Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2235753: cache has only 0 modules 91%|█████████▏| 1103/1208 [14:05:52<1:10:10, 40.10s/it] {'loss': 0.0013, 'grad_norm': 0.25019439670790516, 'learning_rate': 8.69205298013245e-08, 'completion_length': 99.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03192138671875, 'clip_ratio': 0.0, 'epoch': 7.3} 91%|█████████▏| 1103/1208 [14:05:52<1:10:10, 40.10s/it]Start loss calc for inst: open landlanp Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2236626: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open landlanp'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2237499: cache has only 0 modules [Step 1103] loss_orig = 0.001935, loss_refine = -1.205193 [Step 1103] loss_orig = 0.001656, loss_refine = 0.728063[Step 1103] loss_orig = 0.001372, loss_refine = 0.726272[Step 1103] loss_orig = 0.002659, loss_refine = -1.204476[Step 1103] loss_orig = 0.001790, loss_refine = 0.725690 [Step 1103] loss_orig = 0.000455, loss_refine = 0.726410 [Step 1103] loss_orig = 0.002596, loss_refine = 0.726812 [Step 1103] loss_orig = 0.001987, loss_refine = -1.205787 Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2238372: cache has only 0 modules 91%|█████████▏| 1104/1208 [14:06:45<1:16:06, 43.91s/it] {'loss': 0.0016, 'grad_norm': 7.815945530752376, 'learning_rate': 8.609271523178807e-08, 'completion_length': 96.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.7916666666666665, 'reward_std': 0.17251638571421304, 'kl': 0.0350341796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.31} 91%|█████████▏| 1104/1208 [14:06:45<1:16:06, 43.91s/it]Start loss calc for inst: click the UI element (003) Black / Black / Black Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2239245: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element (003) Black / Black / Black'. Your previous answer was [{'action': 'click', 'coordinate': [1379, 585]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2240118: cache has only 0 modules [Step 1104] loss_orig = 0.001178, loss_refine = -0.838505[Step 1104] loss_orig = 0.001231, loss_refine = -2.182014[Step 1104] loss_orig = 0.001666, loss_refine = 0.505683[Step 1104] loss_orig = 0.004197, loss_refine = 0.505665 [Step 1104] loss_orig = 0.002693, loss_refine = 0.505200 [Step 1104] loss_orig = 0.001905, loss_refine = 0.504978[Step 1104] loss_orig = 0.001083, loss_refine = 0.506756 [Step 1104] loss_orig = 0.001207, loss_refine = 0.505153 Start loss calc for inst: click the UI element Crop Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2240991: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Crop'. Your previous answer was [{'action': 'click', 'coordinate': [998, 108]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2241864: cache has only 0 modules [Step 1104] loss_orig = 0.002496, loss_refine = -1.133974 [Step 1104] loss_orig = 0.002337, loss_refine = -0.124427 [Step 1104] loss_orig = 0.001855, loss_refine = -0.122889[Step 1104] loss_orig = 0.002447, loss_refine = 0.934521 [Step 1104] loss_orig = 0.001962, loss_refine = -0.124110 [Step 1104] loss_orig = 0.001338, loss_refine = -0.124600 [Step 1104] loss_orig = 0.001271, loss_refine = -1.132041 [Step 1104] loss_orig = 0.003398, loss_refine = 1.893267 91%|█████████▏| 1105/1208 [14:08:23<1:43:17, 60.17s/it] {'loss': 0.0049, 'grad_norm': 14.671999657427726, 'learning_rate': 8.526490066225166e-08, 'completion_length': 117.96875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.03125, 'rewards/format_reward': 0.96875, 'reward': 2.0625, 'reward_std': 0.4337637573480606, 'kl': 0.0504150390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.32} 91%|█████████▏| 1105/1208 [14:08:23<1:43:17, 60.17s/it]Start loss calc for inst: click the UI element Google Maps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2242737: cache has only 0 modules Start loss calc for inst: click the UI element System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2243610: cache has only 0 modules 92%|█████████▏| 1106/1208 [14:09:12<1:36:28, 56.75s/it] {'loss': 0.002, 'grad_norm': 4.707167791893407, 'learning_rate': 8.443708609271523e-08, 'completion_length': 113.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0504150390625, 'clip_ratio': 0.0, 'epoch': 7.32} 92%|█████████▏| 1106/1208 [14:09:12<1:36:28, 56.75s/it]Start loss calc for inst: switch to a new scence Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2244483: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'switch to a new scence'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2245356: cache has only 0 modules [Step 1106] loss_orig = 0.000730, loss_refine = 0.354456 [Step 1106] loss_orig = 0.000672, loss_refine = -2.472542[Step 1106] loss_orig = 0.000469, loss_refine = 0.356751[Step 1106] loss_orig = 0.000372, loss_refine = 0.353837 [Step 1106] loss_orig = 0.001134, loss_refine = 0.354572[Step 1106] loss_orig = 0.000372, loss_refine = 0.354998 [Step 1106] loss_orig = 0.000938, loss_refine = 0.354763 [Step 1106] loss_orig = 0.001437, loss_refine = 0.353969 Start loss calc for inst: click the UI element How Google handles government requests for user information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2246229: cache has only 0 modules 92%|█████████▏| 1107/1208 [14:10:08<1:35:01, 56.45s/it] {'loss': 0.0014, 'grad_norm': 8.512840766528353, 'learning_rate': 8.36092715231788e-08, 'completion_length': 89.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.027099609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.33} 92%|█████████▏| 1107/1208 [14:10:08<1:35:01, 56.45s/it]Start loss calc for inst: click the UI element Intense Emphasis Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2247102: cache has only 0 modules Start loss calc for inst: click the UI element Undo Increase Indent Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2247975: cache has only 0 modules 92%|█████████▏| 1108/1208 [14:11:02<1:32:50, 55.71s/it] {'loss': 0.0011, 'grad_norm': 3.2778159369144544, 'learning_rate': 8.278145695364239e-08, 'completion_length': 135.25, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 0.875, 'reward': 2.625, 'reward_std': 0.6943650841712952, 'kl': 0.028076171875, 'clip_ratio': 0.0, 'epoch': 7.34} 92%|█████████▏| 1108/1208 [14:11:02<1:32:50, 55.71s/it]Start loss calc for inst: enter settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2248848: cache has only 0 modules Start loss calc for inst: click the UI element Search by image Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2249721: cache has only 0 modules 92%|█████████▏| 1109/1208 [14:11:49<1:28:02, 53.36s/it] {'loss': 0.0017, 'grad_norm': 4.733814119623519, 'learning_rate': 8.195364238410596e-08, 'completion_length': 109.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.043212890625, 'clip_ratio': 0.0, 'epoch': 7.34} 92%|█████████▏| 1109/1208 [14:11:49<1:28:02, 53.36s/it]Start loss calc for inst: click the UI element Layout Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2250594: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2251467: cache has only 0 modules 92%|█████████▏| 1110/1208 [14:12:30<1:21:06, 49.66s/it] {'loss': 0.0014, 'grad_norm': 0.4587650186806682, 'learning_rate': 8.112582781456953e-08, 'completion_length': 91.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'epoch': 7.35} 92%|█████████▏| 1110/1208 [14:12:30<1:21:06, 49.66s/it]Start loss calc for inst: send a smill heart emoji Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2252340: cache has only 0 modules Start loss calc for inst: click the UI element Settings - System Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2253213: cache has only 0 modules 92%|█████████▏| 1111/1208 [14:13:12<1:16:14, 47.16s/it] {'loss': 0.0015, 'grad_norm': 24.140807262542484, 'learning_rate': 8.029801324503311e-08, 'completion_length': 98.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.4355512708425522, 'kl': 0.03680419921875, 'clip_ratio': 0.0, 'epoch': 7.36} 92%|█████████▏| 1111/1208 [14:13:12<1:16:14, 47.16s/it]Start loss calc for inst: click the UI element Format Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2254086: cache has only 0 modules Start loss calc for inst: click the UI element Pause Your Amazon Prime Membership Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2254959: cache has only 0 modules 92%|█████████▏| 1112/1208 [14:14:05<1:18:32, 49.09s/it] {'loss': 0.0028, 'grad_norm': 13.77267015722658, 'learning_rate': 7.947019867549669e-08, 'completion_length': 116.4375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.5303300768136978, 'kl': 0.0712890625, 'clip_ratio': 0.0, 'epoch': 7.36} 92%|█████████▏| 1112/1208 [14:14:05<1:18:32, 49.09s/it]Start loss calc for inst: click the UI element Action Center, 2 new notifications Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2255832: cache has only 0 modules ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Action Center, 2 new notifications'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image.⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. ⚠️ Annotation failed, using original image. closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2256705: cache has only 0 modules [Step 1112] loss_orig = -0.347245, loss_refine = 0.725714[Step 1112] loss_orig = -0.352655, loss_refine = -1.202743[Step 1112] loss_orig = -0.351624, loss_refine = 0.735909[Step 1112] loss_orig = -0.351839, loss_refine = -1.205551 [Step 1112] loss_orig = -0.349060, loss_refine = 0.726583 [Step 1112] loss_orig = -0.351159, loss_refine = -1.205842 [Step 1112] loss_orig = -0.348219, loss_refine = 0.727065 [Step 1112] loss_orig = 2.476028, loss_refine = 0.728708 Start loss calc for inst: switch to show link attributes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2257578: cache has only 0 modules 92%|█████████▏| 1113/1208 [14:15:14<1:27:10, 55.06s/it] {'loss': 0.0023, 'grad_norm': 7.740917106853489, 'learning_rate': 7.864238410596026e-08, 'completion_length': 108.75, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.375, 'reward_std': 0.4082186420758565, 'kl': 0.04937744140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.37} 92%|█████████▏| 1113/1208 [14:15:14<1:27:10, 55.06s/it]Start loss calc for inst: click the UI element Learn more about Authorized Buyers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2258451: cache has only 0 modules Start loss calc for inst: write a message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2259324: cache has only 0 modules 92%|█████████▏| 1114/1208 [14:15:49<1:16:38, 48.92s/it] {'loss': 0.0009, 'grad_norm': 6.235002384777022, 'learning_rate': 7.781456953642383e-08, 'completion_length': 94.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0225830078125, 'clip_ratio': 0.0, 'epoch': 7.38} 92%|█████████▏| 1114/1208 [14:15:49<1:16:38, 48.92s/it]Start loss calc for inst: click the UI element Sign in - Google Accounts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2260197: cache has only 0 modules Start loss calc for inst: click the UI element Close pane Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2261070: cache has only 0 modules 92%|█████████▏| 1115/1208 [14:16:28<1:11:16, 45.99s/it] {'loss': 0.0018, 'grad_norm': 6.035061342804686, 'learning_rate': 7.698675496688742e-08, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.044921875, 'clip_ratio': 0.0, 'epoch': 7.38} 92%|█████████▏| 1115/1208 [14:16:28<1:11:16, 45.99s/it]Start loss calc for inst: share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2261943: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'share'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2262816: cache has only 0 modules [Step 1115] loss_orig = 0.000955, loss_refine = 0.000654[Step 1115] loss_orig = 0.001617, loss_refine = 0.001243 [Step 1115] loss_orig = 0.001368, loss_refine = 0.000349[Step 1115] loss_orig = 0.001705, loss_refine = 0.002569 [Step 1115] loss_orig = 0.001259, loss_refine = 0.001312 [Step 1115] loss_orig = 0.000833, loss_refine = 0.002784[Step 1115] loss_orig = 0.001724, loss_refine = 0.001011 [Step 1115] loss_orig = 0.001069, loss_refine = 0.000366 Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2263689: cache has only 0 modules 92%|█████████▏| 1116/1208 [14:17:18<1:12:30, 47.29s/it] {'loss': 0.0009, 'grad_norm': 0.21649857734747133, 'learning_rate': 7.615894039735099e-08, 'completion_length': 83.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.3333333333333335, 'reward_std': 0.0, 'kl': 0.023162841796875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 7.39} 92%|█████████▏| 1116/1208 [14:17:18<1:12:30, 47.29s/it]Start loss calc for inst: fold input method Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2264562: cache has only 0 modules Start loss calc for inst: click the UI element 100% (Recommended) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2265435: cache has only 0 modules 92%|█████████▏| 1117/1208 [14:18:04<1:10:53, 46.74s/it] {'loss': 0.001, 'grad_norm': 2.7204187476710553, 'learning_rate': 7.533112582781457e-08, 'completion_length': 104.9375, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9375, 'reward': 2.5, 'reward_std': 0.4629100561141968, 'kl': 0.0247802734375, 'clip_ratio': 0.0, 'epoch': 7.4} 92%|█████████▏| 1117/1208 [14:18:04<1:10:53, 46.74s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2266308: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: BadgeAnchorLargeTicker Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2267181: cache has only 0 modules 93%|█████████▎| 1118/1208 [14:18:53<1:10:59, 47.33s/it] {'loss': 0.0014, 'grad_norm': 21.610067066872013, 'learning_rate': 7.450331125827815e-08, 'completion_length': 107.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.5175491571426392, 'kl': 0.0361328125, 'clip_ratio': 0.0, 'epoch': 7.4} 93%|█████████▎| 1118/1208 [14:18:53<1:10:59, 47.33s/it]Start loss calc for inst: click the UI element Thunderbird Mail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2268054: cache has only 0 modules Start loss calc for inst: click the UI element Copy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2268927: cache has only 0 modules 93%|█████████▎| 1119/1208 [14:19:30<1:05:43, 44.31s/it] {'loss': 0.001, 'grad_norm': 5.460839372795647, 'learning_rate': 7.367549668874172e-08, 'completion_length': 86.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02569580078125, 'clip_ratio': 0.0, 'epoch': 7.41} 93%|█████████▎| 1119/1208 [14:19:30<1:05:43, 44.31s/it]Start loss calc for inst: click the UI element References Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2269800: cache has only 0 modules Start loss calc for inst: add a new item Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2270673: cache has only 0 modules 93%|█████████▎| 1120/1208 [14:20:16<1:05:43, 44.82s/it] {'loss': 0.0018, 'grad_norm': 8.871706281307882, 'learning_rate': 7.284768211920529e-08, 'completion_length': 91.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0438232421875, 'clip_ratio': 0.0, 'epoch': 7.42} 93%|█████████▎| 1120/1208 [14:20:16<1:05:43, 44.82s/it]Start loss calc for inst: customize focus time Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2271546: cache has only 0 modules Start loss calc for inst: add a new file Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2272419: cache has only 0 modules 93%|█████████▎| 1121/1208 [14:20:52<1:01:20, 42.30s/it] {'loss': 0.0012, 'grad_norm': 0.17929328260244737, 'learning_rate': 7.201986754966886e-08, 'completion_length': 80.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03125, 'clip_ratio': 0.0, 'epoch': 7.42} 93%|█████████▎| 1121/1208 [14:20:52<1:01:20, 42.30s/it]Start loss calc for inst: click the UI element AutomationID: Icons_3dGlasses Reward function name: accuracy_reward_action Reward: 0.75 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.75 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2273292: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_3dGlasses'. Your previous answer was [{'action': 'click', 'coordinate': [447, 448]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2274165: cache has only 0 modules [Step 1121] loss_orig = -0.538248, loss_refine = -0.351360[Step 1121] loss_orig = 1.621144, loss_refine = -0.349436 [Step 1121] loss_orig = -0.538727, loss_refine = -0.348967 [Step 1121] loss_orig = -0.538421, loss_refine = -0.352002 [Step 1121] loss_orig = 1.624379, loss_refine = 2.475791 [Step 1121] loss_orig = -0.538518, loss_refine = -0.351667 [Step 1121] loss_orig = -0.538869, loss_refine = -0.350972 [Step 1121] loss_orig = -0.538295, loss_refine = -0.350986 Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2275038: cache has only 0 modules 93%|█████████▎| 1122/1208 [14:22:05<1:13:31, 51.29s/it] {'loss': 0.0025, 'grad_norm': 5.046389339780777, 'learning_rate': 7.119205298013245e-08, 'completion_length': 117.58333333333333, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 0.9166666666666666, 'reward': 2.7916666666666665, 'reward_std': 0.42645783225695294, 'kl': 0.0528564453125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.43} 93%|█████████▎| 1122/1208 [14:22:05<1:13:31, 51.29s/it]Start loss calc for inst: previous song Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2275911: cache has only 0 modules Start loss calc for inst: click the UI element Shape Outline Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2276784: cache has only 0 modules 93%|█████████▎| 1123/1208 [14:22:54<1:11:43, 50.63s/it] {'loss': 0.0015, 'grad_norm': 4.846505685628728, 'learning_rate': 7.036423841059602e-08, 'completion_length': 102.75, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.4375, 'reward_std': 0.4172614812850952, 'kl': 0.0372314453125, 'clip_ratio': 0.0, 'epoch': 7.44} 93%|█████████▎| 1123/1208 [14:22:54<1:11:43, 50.63s/it]Start loss calc for inst: open dynamic shot Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2277657: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_ArrowCircle_M Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2278530: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_ArrowCircle_M'. Your previous answer was [{'action': 'click', 'coordinate': [333, 910]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2279403: cache has only 0 modules [Step 1123] loss_orig = 0.001264, loss_refine = 0.001960[Step 1123] loss_orig = 0.001512, loss_refine = 0.003055[Step 1123] loss_orig = 0.001359, loss_refine = 0.002551 [Step 1123] loss_orig = 0.002590, loss_refine = 0.002752 [Step 1123] loss_orig = 0.002344, loss_refine = 0.002122 [Step 1123] loss_orig = 0.001027, loss_refine = 0.002381 [Step 1123] loss_orig = 0.001419, loss_refine = 0.001941 [Step 1123] loss_orig = 0.004033, loss_refine = 0.001573 93%|█████████▎| 1124/1208 [14:24:12<1:22:43, 59.08s/it] {'loss': 0.0024, 'grad_norm': 16.61616520021768, 'learning_rate': 6.95364238410596e-08, 'completion_length': 115.08333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 1.0, 'reward': 2.7083333333333335, 'reward_std': 0.11785112818082173, 'kl': 0.05517578125, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.44} 93%|█████████▎| 1124/1208 [14:24:12<1:22:43, 59.08s/it]Start loss calc for inst: 1 Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2280276: cache has only 0 modules Start loss calc for inst: click the UI element hooters casino las vegas Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2281149: cache has only 0 modules 93%|█████████▎| 1125/1208 [14:24:51<1:13:19, 53.01s/it] {'loss': 0.0022, 'grad_norm': 3.7955125012100797, 'learning_rate': 6.870860927152318e-08, 'completion_length': 103.25, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.26726123690605164, 'kl': 0.05474853515625, 'clip_ratio': 0.0, 'epoch': 7.45} 93%|█████████▎| 1125/1208 [14:24:51<1:13:19, 53.01s/it]Start loss calc for inst: click the UI element deserts Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2282022: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element deserts'. Your previous answer was [{'action': 'click', 'coordinate': [1060, 519]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.75 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2282895: cache has only 0 modules [Step 1125] loss_orig = 0.004022, loss_refine = -0.539138 [Step 1125] loss_orig = 0.001856, loss_refine = -0.538926 [Step 1125] loss_orig = 0.002305, loss_refine = -0.538832[Step 1125] loss_orig = 0.003730, loss_refine = -0.539051 [Step 1125] loss_orig = 0.001332, loss_refine = -0.539345[Step 1125] loss_orig = 0.001713, loss_refine = -0.537915 [Step 1125] loss_orig = 0.008610, loss_refine = 1.621206 [Step 1125] loss_orig = 0.006700, loss_refine = 1.621765 Start loss calc for inst: click the UI element Decorative Locked Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2283768: cache has only 0 modules 93%|█████████▎| 1126/1208 [14:25:54<1:16:20, 55.86s/it] {'loss': 0.0018, 'grad_norm': 8.215221273821127, 'learning_rate': 6.788079470198675e-08, 'completion_length': 97.75, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.48678086201349896, 'kl': 0.0758056640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.75, 'epoch': 7.46} 93%|█████████▎| 1126/1208 [14:25:54<1:16:20, 55.86s/it]Start loss calc for inst: click the UI element +18 more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2284641: cache has only 0 modules Start loss calc for inst: add alarm to the included controls Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2285514: cache has only 0 modules 93%|█████████▎| 1127/1208 [14:26:40<1:11:17, 52.81s/it] {'loss': 0.0014, 'grad_norm': 9.452647168141661, 'learning_rate': 6.705298013245032e-08, 'completion_length': 100.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.44403792917728424, 'kl': 0.03411865234375, 'clip_ratio': 0.0, 'epoch': 7.46} 93%|█████████▎| 1127/1208 [14:26:40<1:11:17, 52.81s/it]Start loss calc for inst: click the UI element AutomationID: rh_meter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2286387: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: rh_meter'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2287260: cache has only 0 modules [Step 1127] loss_orig = -0.350970, loss_refine = 0.196330[Step 1127] loss_orig = -0.347349, loss_refine = 0.196770 [Step 1127] loss_orig = -0.348597, loss_refine = 1.792168 [Step 1127] loss_orig = -0.352274, loss_refine = -1.362939 [Step 1127] loss_orig = 2.475515, loss_refine = 0.196942 [Step 1127] loss_orig = -0.351667, loss_refine = 0.198160 [Step 1127] loss_orig = -0.351143, loss_refine = -1.364098 [Step 1127] loss_orig = -0.350222, loss_refine = 0.197040 Start loss calc for inst: click the UI element 11870934/1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2288133: cache has only 0 modules 93%|█████████▎| 1128/1208 [14:27:43<1:14:30, 55.88s/it] {'loss': 0.0034, 'grad_norm': 6.094045148110627, 'learning_rate': 6.62251655629139e-08, 'completion_length': 109.79166666666667, 'rewards/accuracy_reward_action': 0.9166666666666666, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.2916666666666665, 'reward_std': 0.4493255813916524, 'kl': 0.04339599609375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.47} 93%|█████████▎| 1128/1208 [14:27:43<1:14:30, 55.88s/it]Start loss calc for inst: click the UI element Share Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2289006: cache has only 0 modules Start loss calc for inst: click the UI element My Watchlist Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2289879: cache has only 0 modules 93%|█████████▎| 1129/1208 [14:28:24<1:07:48, 51.50s/it] {'loss': 0.0007, 'grad_norm': 0.20936360238538232, 'learning_rate': 6.539735099337748e-08, 'completion_length': 93.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0184326171875, 'clip_ratio': 0.0, 'epoch': 7.48} 93%|█████████▎| 1129/1208 [14:28:24<1:07:48, 51.50s/it]Start loss calc for inst: random music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2290752: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'random music'. Your previous answer was [{'action': 'click', 'coordinate': [1029, 589]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2291625: cache has only 0 modules [Step 1129] loss_orig = 0.002058, loss_refine = 0.936395[Step 1129] loss_orig = 0.001160, loss_refine = -0.934125[Step 1129] loss_orig = 0.001691, loss_refine = 0.936831 [Step 1129] loss_orig = 0.001363, loss_refine = 0.939201[Step 1129] loss_orig = 0.002526, loss_refine = 0.936930 [Step 1129] loss_orig = 0.001763, loss_refine = -0.934412 [Step 1129] loss_orig = 0.001598, loss_refine = -0.934521 [Step 1129] loss_orig = 0.003837, loss_refine = -0.934455 Start loss calc for inst: click the UI element Page 1 content Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2292498: cache has only 0 modules 94%|█████████▎| 1130/1208 [14:29:18<1:08:05, 52.38s/it] {'loss': 0.0014, 'grad_norm': 8.038281903343698, 'learning_rate': 6.456953642384106e-08, 'completion_length': 94.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.47419944405555725, 'kl': 0.0426025390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 7.48} 94%|█████████▎| 1130/1208 [14:29:18<1:08:05, 52.38s/it]Start loss calc for inst: display all photos  Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2293371: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'display all photos '. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2294244: cache has only 0 modules [Step 1130] loss_orig = 0.000767, loss_refine = 0.001785[Step 1130] loss_orig = 0.000383, loss_refine = 0.002301 [Step 1130] loss_orig = 0.000270, loss_refine = 0.002953[Step 1130] loss_orig = 0.000520, loss_refine = 0.002269 [Step 1130] loss_orig = 0.000456, loss_refine = 0.001561 [Step 1130] loss_orig = 0.000411, loss_refine = 0.001544 [Step 1130] loss_orig = 0.000265, loss_refine = 0.002495 [Step 1130] loss_orig = 0.000398, loss_refine = 0.003762 Start loss calc for inst: display more functional icon Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2295117: cache has only 0 modules 94%|█████████▎| 1131/1208 [14:30:03<1:04:14, 50.06s/it] {'loss': 0.0028, 'grad_norm': 0.2795082631925892, 'learning_rate': 6.374172185430464e-08, 'completion_length': 76.83333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.6666666666666666, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.04644775390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.49} 94%|█████████▎| 1131/1208 [14:30:03<1:04:14, 50.06s/it]Start loss calc for inst: click the UI element Kopieer skakel Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2295990: cache has only 0 modules Start loss calc for inst: click the UI element Gray Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2296863: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Gray'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.625 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2297736: cache has only 0 modules [Step 1131] loss_orig = -0.351378, loss_refine = -0.722490[Step 1131] loss_orig = -0.351763, loss_refine = -0.723523 [Step 1131] loss_orig = -0.352483, loss_refine = 1.208825 [Step 1131] loss_orig = -0.352663, loss_refine = -0.723388[Step 1131] loss_orig = -0.352547, loss_refine = -0.723211 [Step 1131] loss_orig = -0.351965, loss_refine = 1.210417 [Step 1131] loss_orig = -0.351916, loss_refine = -0.723320 [Step 1131] loss_orig = 2.475060, loss_refine = 1.210406 94%|█████████▎| 1132/1208 [14:31:05<1:07:59, 53.68s/it] {'loss': 0.0014, 'grad_norm': 6.513086371015526, 'learning_rate': 6.291390728476821e-08, 'completion_length': 99.16666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.5, 'reward_std': 0.2903675138950348, 'kl': 0.02947998046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.625, 'epoch': 7.5} 94%|█████████▎| 1132/1208 [14:31:05<1:07:59, 53.68s/it]Start loss calc for inst: click the UI element Dislike Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2298609: cache has only 0 modules Start loss calc for inst: click the UI element Line History View, group Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2299482: cache has only 0 modules 94%|█████████▍| 1133/1208 [14:31:48<1:03:10, 50.54s/it] {'loss': 0.0018, 'grad_norm': 6.5695474396840225, 'learning_rate': 6.208609271523178e-08, 'completion_length': 105.8125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 0.9375, 'reward': 2.4375, 'reward_std': 0.4172614812850952, 'kl': 0.0457763671875, 'clip_ratio': 0.0, 'epoch': 7.5} 94%|█████████▍| 1133/1208 [14:31:48<1:03:10, 50.54s/it]Start loss calc for inst: click the UI element 773 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2300355: cache has only 0 modules Start loss calc for inst: click the UI element Minimize Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2301228: cache has only 0 modules 94%|█████████▍| 1134/1208 [14:32:34<1:00:41, 49.21s/it] {'loss': 0.0013, 'grad_norm': 0.34627258604770894, 'learning_rate': 6.125827814569537e-08, 'completion_length': 122.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03363037109375, 'clip_ratio': 0.0, 'epoch': 7.51} 94%|█████████▍| 1134/1208 [14:32:34<1:00:41, 49.21s/it]Start loss calc for inst: click the UI element poe pc Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2302101: cache has only 0 modules Start loss calc for inst: click the UI element Strong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2302974: cache has only 0 modules 94%|█████████▍| 1135/1208 [14:33:21<58:47, 48.32s/it] {'loss': 0.0012, 'grad_norm': 1.8008959149220072, 'learning_rate': 6.043046357615894e-08, 'completion_length': 106.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0308837890625, 'clip_ratio': 0.0, 'epoch': 7.52} 94%|█████████▍| 1135/1208 [14:33:21<58:47, 48.32s/it]Start loss calc for inst: click the UI element Footer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2303847: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft Edge Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2304720: cache has only 0 modules 94%|█████████▍| 1136/1208 [14:34:12<59:15, 49.38s/it] {'loss': 0.0034, 'grad_norm': 7.314697678648334, 'learning_rate': 5.960264900662251e-08, 'completion_length': 96.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0849609375, 'clip_ratio': 0.0, 'epoch': 7.52} 94%|█████████▍| 1136/1208 [14:34:12<59:15, 49.38s/it]Start loss calc for inst: open app automatic download Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2305593: cache has only 0 modules Start loss calc for inst: click the UI element Undo Apply Quick Style Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2306466: cache has only 0 modules 94%|█████████▍| 1137/1208 [14:35:04<59:14, 50.07s/it] {'loss': 0.0012, 'grad_norm': 5.634641779844818, 'learning_rate': 5.877483443708609e-08, 'completion_length': 109.5625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.8125, 'reward_std': 0.5303300619125366, 'kl': 0.0291748046875, 'clip_ratio': 0.0, 'epoch': 7.53} 94%|█████████▍| 1137/1208 [14:35:04<59:14, 50.07s/it]Start loss calc for inst: click the UI element Sky Blue Bikes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2307339: cache has only 0 modules Start loss calc for inst: click the UI element Font Name Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2308212: cache has only 0 modules 94%|█████████▍| 1138/1208 [14:35:42<54:13, 46.48s/it] {'loss': 0.0012, 'grad_norm': 5.926307852666247, 'learning_rate': 5.7947019867549666e-08, 'completion_length': 105.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.03033447265625, 'clip_ratio': 0.0, 'epoch': 7.54} 94%|█████████▍| 1138/1208 [14:35:42<54:13, 46.48s/it]Start loss calc for inst: open memo app Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2309085: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'open memo app'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2309958: cache has only 0 modules [Step 1138] loss_orig = 0.001461, loss_refine = 0.000961[Step 1138] loss_orig = 0.000364, loss_refine = 0.001805[Step 1138] loss_orig = 0.000849, loss_refine = 0.000716 [Step 1138] loss_orig = 0.001757, loss_refine = 0.001298 [Step 1138] loss_orig = 0.000833, loss_refine = 0.000599[Step 1138] loss_orig = 0.000394, loss_refine = 0.000605 [Step 1138] loss_orig = 0.002044, loss_refine = 0.003009 [Step 1138] loss_orig = 0.000822, loss_refine = 0.002221 Start loss calc for inst: click the UI element Get More Storage. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2310831: cache has only 0 modules 94%|█████████▍| 1139/1208 [14:36:36<55:59, 48.69s/it] {'loss': 0.001, 'grad_norm': 6.4702357578298635, 'learning_rate': 5.711920529801324e-08, 'completion_length': 81.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.6666666666666665, 'reward_std': 0.0, 'kl': 0.02142333984375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 7.54} 94%|█████████▍| 1139/1208 [14:36:36<55:59, 48.69s/it]Start loss calc for inst: click the UI element Recommended Design: Design Idea Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2311704: cache has only 0 modules Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2312577: cache has only 0 modules 94%|█████████▍| 1140/1208 [14:37:23<54:42, 48.28s/it] {'loss': 0.0015, 'grad_norm': 3.860101523336089, 'learning_rate': 5.629139072847682e-08, 'completion_length': 110.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03802490234375, 'clip_ratio': 0.0, 'epoch': 7.55} 94%|█████████▍| 1140/1208 [14:37:23<54:42, 48.28s/it]Start loss calc for inst: click the UI element Search for stocks, ETFs & more Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2313450: cache has only 0 modules Start loss calc for inst: click the UI element Disability Services Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2314323: cache has only 0 modules 94%|█████████▍| 1141/1208 [14:38:12<53:55, 48.29s/it] {'loss': 0.0009, 'grad_norm': 3.169725297650063, 'learning_rate': 5.5463576158940396e-08, 'completion_length': 115.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.02154541015625, 'clip_ratio': 0.0, 'epoch': 7.56} 94%|█████████▍| 1141/1208 [14:38:12<53:55, 48.29s/it]Start loss calc for inst: click the UI element New Tab Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2315196: cache has only 0 modules Start loss calc for inst: sequential music playback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2316069: cache has only 0 modules 95%|█████████▍| 1142/1208 [14:38:47<48:55, 44.47s/it] {'loss': 0.009, 'grad_norm': 7.128654374517998, 'learning_rate': 5.463576158940397e-08, 'completion_length': 99.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.408231720328331, 'kl': 0.224853515625, 'clip_ratio': 0.0, 'epoch': 7.56} 95%|█████████▍| 1142/1208 [14:38:47<48:55, 44.47s/it]Start loss calc for inst: search history Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2316942: cache has only 0 modules Start loss calc for inst: click the UI element Xiaomi Redmi Note 13 Pro Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2317815: cache has only 0 modules 95%|█████████▍| 1143/1208 [14:39:27<46:46, 43.18s/it] {'loss': 0.0016, 'grad_norm': 0.8641207288361356, 'learning_rate': 5.380794701986754e-08, 'completion_length': 101.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0400390625, 'clip_ratio': 0.0, 'epoch': 7.57} 95%|█████████▍| 1143/1208 [14:39:27<46:46, 43.18s/it]Start loss calc for inst: more details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2318688: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'more details'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2319561: cache has only 0 modules [Step 1143] loss_orig = 0.001292, loss_refine = 0.726168[Step 1143] loss_orig = 0.001584, loss_refine = 0.726917 [Step 1143] loss_orig = 0.003629, loss_refine = -1.206148 [Step 1143] loss_orig = 0.001431, loss_refine = 0.725280 [Step 1143] loss_orig = 0.001216, loss_refine = 0.726885 [Step 1143] loss_orig = 0.002108, loss_refine = -1.204223 [Step 1143] loss_orig = 0.001879, loss_refine = 0.725678 [Step 1143] loss_orig = 0.001420, loss_refine = -1.204613 Start loss calc for inst: click the UI element Multiple reviewers in pull requests Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2320434: cache has only 0 modules 95%|█████████▍| 1144/1208 [14:40:24<50:14, 47.11s/it] {'loss': 0.0018, 'grad_norm': 13.323017575495806, 'learning_rate': 5.2980132450331126e-08, 'completion_length': 88.58333333333333, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.3450327714284261, 'kl': 0.04248046875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.58} 95%|█████████▍| 1144/1208 [14:40:24<50:14, 47.11s/it]Start loss calc for inst: show all message Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2321307: cache has only 0 modules Start loss calc for inst: add this song to favorite Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2322180: cache has only 0 modules 95%|█████████▍| 1145/1208 [14:40:58<45:28, 43.31s/it] {'loss': 0.0013, 'grad_norm': 8.34688294647377, 'learning_rate': 5.21523178807947e-08, 'completion_length': 88.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03369140625, 'clip_ratio': 0.0, 'epoch': 7.58} 95%|█████████▍| 1145/1208 [14:40:58<45:28, 43.31s/it]Start loss calc for inst: click the UI element Cool grey Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2323053: cache has only 0 modules Start loss calc for inst: click the UI element Click Review setting. Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2323926: cache has only 0 modules 95%|█████████▍| 1146/1208 [14:41:33<42:00, 40.66s/it] {'loss': 0.001, 'grad_norm': 0.18453429362488255, 'learning_rate': 5.132450331125827e-08, 'completion_length': 94.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0257568359375, 'clip_ratio': 0.0, 'epoch': 7.59} 95%|█████████▍| 1146/1208 [14:41:33<42:00, 40.66s/it]Start loss calc for inst: click the UI element MORE Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2324799: cache has only 0 modules Start loss calc for inst: click the UI element Repository rules Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2325672: cache has only 0 modules 95%|█████████▍| 1147/1208 [14:42:11<40:35, 39.93s/it] {'loss': 0.0008, 'grad_norm': 0.20429681016686516, 'learning_rate': 5.0496688741721856e-08, 'completion_length': 90.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.019744873046875, 'clip_ratio': 0.0, 'epoch': 7.6} 95%|█████████▍| 1147/1208 [14:42:11<40:35, 39.93s/it]Start loss calc for inst: click the UI element Slack Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2326545: cache has only 0 modules Start loss calc for inst: open clock at 3 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2327418: cache has only 0 modules 95%|█████████▌| 1148/1208 [14:42:45<38:05, 38.09s/it] {'loss': 0.0028, 'grad_norm': 14.644063475521918, 'learning_rate': 4.9668874172185426e-08, 'completion_length': 83.6875, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.375, 'rewards/format_reward': 0.9375, 'reward': 2.25, 'reward_std': 0.5487885922193527, 'kl': 0.0693359375, 'clip_ratio': 0.0, 'epoch': 7.6} 95%|█████████▌| 1148/1208 [14:42:45<38:05, 38.09s/it]Start loss calc for inst: click the UI element Advertise Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2328291: cache has only 0 modules Start loss calc for inst: click the UI element Feedback Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2329164: cache has only 0 modules 95%|█████████▌| 1149/1208 [14:43:21<37:02, 37.67s/it] {'loss': 0.0009, 'grad_norm': 0.6993597208644131, 'learning_rate': 4.8841059602649e-08, 'completion_length': 89.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.022735595703125, 'clip_ratio': 0.0, 'epoch': 7.61} 95%|█████████▌| 1149/1208 [14:43:21<37:02, 37.67s/it]Start loss calc for inst: click the UI element Change Picture Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2330037: cache has only 0 modules Start loss calc for inst: click the UI element Code of Conduct Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2330910: cache has only 0 modules 95%|█████████▌| 1150/1208 [14:43:59<36:15, 37.51s/it] {'loss': 0.0008, 'grad_norm': 9.250292928326964, 'learning_rate': 4.8013245033112586e-08, 'completion_length': 89.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.01959228515625, 'clip_ratio': 0.0, 'epoch': 7.62} 95%|█████████▌| 1150/1208 [14:43:59<36:15, 37.51s/it]Start loss calc for inst: check the information about airtag Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2331783: cache has only 0 modules Start loss calc for inst: select source language Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2332656: cache has only 0 modules 95%|█████████▌| 1151/1208 [14:44:33<34:42, 36.53s/it] {'loss': 0.0013, 'grad_norm': 0.1916034452297187, 'learning_rate': 4.7185430463576156e-08, 'completion_length': 83.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0335693359375, 'clip_ratio': 0.0, 'epoch': 7.62} 95%|█████████▌| 1151/1208 [14:44:33<34:42, 36.53s/it]Start loss calc for inst: click the UI element Stereo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2333529: cache has only 0 modules Start loss calc for inst: click the UI element Images Allow (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2334402: cache has only 0 modules 95%|█████████▌| 1152/1208 [14:45:05<32:47, 35.14s/it] {'loss': 0.0007, 'grad_norm': 0.24738258741530786, 'learning_rate': 4.635761589403973e-08, 'completion_length': 80.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.016845703125, 'clip_ratio': 0.0, 'epoch': 7.63} 95%|█████████▌| 1152/1208 [14:45:05<32:47, 35.14s/it]Start loss calc for inst: click the UI element Show translate options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2335275: cache has only 0 modules Start loss calc for inst: click the UI element Microsoft search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2336148: cache has only 0 modules 95%|█████████▌| 1153/1208 [14:45:52<35:41, 38.94s/it] {'loss': 0.0026, 'grad_norm': 4.229338652038711, 'learning_rate': 4.55298013245033e-08, 'completion_length': 111.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.06494140625, 'clip_ratio': 0.0, 'epoch': 7.64} 95%|█████████▌| 1153/1208 [14:45:52<35:41, 38.94s/it]Start loss calc for inst: more information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2337021: cache has only 0 modules Start loss calc for inst: check my account Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2337894: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'check my account'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.375 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2338767: cache has only 0 modules [Step 1153] loss_orig = 0.000974, loss_refine = 0.543847[Step 1153] loss_orig = 0.001088, loss_refine = -1.618311 [Step 1153] loss_orig = 0.001470, loss_refine = -0.538426[Step 1153] loss_orig = 0.001459, loss_refine = 0.540849 [Step 1153] loss_orig = 0.001459, loss_refine = 1.621776 [Step 1153] loss_orig = 0.002049, loss_refine = -0.538669 [Step 1153] loss_orig = 0.001084, loss_refine = 0.541108 [Step 1153] loss_orig = 0.000683, loss_refine = -0.538797 96%|█████████▌| 1154/1208 [14:46:45<38:36, 42.89s/it] {'loss': 0.0015, 'grad_norm': 3.6024179528088776, 'learning_rate': 4.4701986754966886e-08, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.4583333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5, 'reward_std': 0.30860670407613117, 'kl': 0.0338134765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.64} 96%|█████████▌| 1154/1208 [14:46:45<38:36, 42.89s/it]Start loss calc for inst: click the UI element Automatic downloads Ask (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2339640: cache has only 0 modules Start loss calc for inst: click the UI element AutomationID: Icons_AnemoneAndClownfish Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2340513: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element AutomationID: Icons_AnemoneAndClownfish'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.125 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2341386: cache has only 0 modules [Step 1154] loss_orig = 0.001694, loss_refine = 0.003096[Step 1154] loss_orig = 0.001358, loss_refine = 0.004202 [Step 1154] loss_orig = 0.003742, loss_refine = -1.868609[Step 1154] loss_orig = 0.001864, loss_refine = 0.002455 [Step 1154] loss_orig = 0.001925, loss_refine = 0.000932 [Step 1154] loss_orig = 0.000791, loss_refine = 0.001372 [Step 1154] loss_orig = 0.001154, loss_refine = 1.871701 [Step 1154] loss_orig = 0.000687, loss_refine = 0.002053 96%|█████████▌| 1155/1208 [14:47:47<43:01, 48.71s/it] {'loss': 0.0015, 'grad_norm': 33.655757102925286, 'learning_rate': 4.387417218543046e-08, 'completion_length': 107.79166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.2960252861181895, 'kl': 0.0322265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.125, 'epoch': 7.65} 96%|█████████▌| 1155/1208 [14:47:47<43:01, 48.71s/it]Start loss calc for inst: start recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2342259: cache has only 0 modules Start loss calc for inst: click the UI element Settings - On startup Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2343132: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Settings - On startup'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2344005: cache has only 0 modules [Step 1155] loss_orig = 0.001196, loss_refine = -0.352070 [Step 1155] loss_orig = 0.000864, loss_refine = -0.350129 [Step 1155] loss_orig = 0.001046, loss_refine = -0.350162[Step 1155] loss_orig = 0.007571, loss_refine = -0.352285[Step 1155] loss_orig = 0.001965, loss_refine = -0.352281 [Step 1155] loss_orig = 0.003585, loss_refine = -0.352297 [Step 1155] loss_orig = 0.000756, loss_refine = -0.352314 [Step 1155] loss_orig = 0.001014, loss_refine = 2.482237 96%|█████████▌| 1156/1208 [14:48:44<44:29, 51.34s/it] {'loss': 0.0019, 'grad_norm': 4.9332454171846285, 'learning_rate': 4.304635761589403e-08, 'completion_length': 97.91666666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.2916666666666665, 'reward_std': 0.11785112818082173, 'kl': 0.0426025390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.0, 'epoch': 7.66} 96%|█████████▌| 1156/1208 [14:48:44<44:29, 51.34s/it]Start loss calc for inst: click the UI element AutomationID: topic-link-a151002 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2344878: cache has only 0 modules Start loss calc for inst: click the UI element Header & Footer... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2345751: cache has only 0 modules 96%|█████████▌| 1157/1208 [14:49:31<42:19, 49.80s/it] {'loss': 0.0013, 'grad_norm': 11.132938011790868, 'learning_rate': 4.2218543046357616e-08, 'completion_length': 117.25, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.408231720328331, 'kl': 0.031494140625, 'clip_ratio': 0.0, 'epoch': 7.66} 96%|█████████▌| 1157/1208 [14:49:31<42:19, 49.80s/it]Start loss calc for inst: screen recorder Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2346624: cache has only 0 modules Start loss calc for inst: click the UI element Master Background Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2347497: cache has only 0 modules 96%|█████████▌| 1158/1208 [14:50:14<39:56, 47.93s/it] {'loss': 0.0017, 'grad_norm': 6.100578709690044, 'learning_rate': 4.139072847682119e-08, 'completion_length': 108.5, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.04156494140625, 'clip_ratio': 0.0, 'epoch': 7.67} 96%|█████████▌| 1158/1208 [14:50:14<39:56, 47.93s/it]Start loss calc for inst: setting up airpods connection Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2348370: cache has only 0 modules Start loss calc for inst: click the UI element View Side by Side Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2349243: cache has only 0 modules 96%|█████████▌| 1159/1208 [14:50:54<37:08, 45.49s/it] {'loss': 0.0008, 'grad_norm': 5.656675832169436, 'learning_rate': 4.056291390728476e-08, 'completion_length': 104.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.020538330078125, 'clip_ratio': 0.0, 'epoch': 7.68} 96%|█████████▌| 1159/1208 [14:50:54<37:08, 45.49s/it]Start loss calc for inst: click the UI element Using a Promotional Code for Amazon Prime Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2350116: cache has only 0 modules Start loss calc for inst: view as year Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2350989: cache has only 0 modules 96%|█████████▌| 1160/1208 [14:51:29<33:59, 42.48s/it] {'loss': 0.0009, 'grad_norm': 0.17275868722184545, 'learning_rate': 3.9735099337748346e-08, 'completion_length': 90.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.02142333984375, 'clip_ratio': 0.0, 'epoch': 7.68} 96%|█████████▌| 1160/1208 [14:51:29<33:59, 42.48s/it]Start loss calc for inst: click the UI element Settings and more (Alt+F) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2351862: cache has only 0 modules Start loss calc for inst: click the UI element Create new... Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2352735: cache has only 0 modules 96%|█████████▌| 1161/1208 [14:52:09<32:36, 41.63s/it] {'loss': 0.0014, 'grad_norm': 0.20909076857219186, 'learning_rate': 3.8907284768211916e-08, 'completion_length': 96.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0357666015625, 'clip_ratio': 0.0, 'epoch': 7.69} 96%|█████████▌| 1161/1208 [14:52:09<32:36, 41.63s/it]Start loss calc for inst: view comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2353608: cache has only 0 modules Start loss calc for inst: click the UI element Use F12 key to open the Developer tools Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2354481: cache has only 0 modules 96%|█████████▌| 1162/1208 [14:52:48<31:22, 40.92s/it] {'loss': 0.001, 'grad_norm': 6.877645131006207, 'learning_rate': 3.807947019867549e-08, 'completion_length': 99.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02435302734375, 'clip_ratio': 0.0, 'epoch': 7.7} 96%|█████████▌| 1162/1208 [14:52:48<31:22, 40.92s/it]Start loss calc for inst: click the UI element Advertise Your Products Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2355354: cache has only 0 modules Start loss calc for inst: click the UI element October 2022 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2356227: cache has only 0 modules 96%|█████████▋| 1163/1208 [14:53:28<30:22, 40.50s/it] {'loss': 0.0015, 'grad_norm': 0.7547181592328596, 'learning_rate': 3.7251655629139076e-08, 'completion_length': 97.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.036865234375, 'clip_ratio': 0.0, 'epoch': 7.7} 96%|█████████▋| 1163/1208 [14:53:28<30:22, 40.50s/it]Start loss calc for inst: exchange target and source city Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2357100: cache has only 0 modules Start loss calc for inst: click the UI element English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2357973: cache has only 0 modules 96%|█████████▋| 1164/1208 [14:54:06<29:12, 39.83s/it] {'loss': 0.001, 'grad_norm': 46.64538513202064, 'learning_rate': 3.6423841059602646e-08, 'completion_length': 88.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.625, 'rewards/format_reward': 1.0, 'reward': 2.625, 'reward_std': 0.2314550280570984, 'kl': 0.02484130859375, 'clip_ratio': 0.0, 'epoch': 7.71} 96%|█████████▋| 1164/1208 [14:54:06<29:12, 39.83s/it]Start loss calc for inst: click the UI element Wikipedia The Free Encyclopedia Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2358846: cache has only 0 modules Start loss calc for inst: click the UI element Cheap Hotels - Save70.com Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2359719: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Cheap Hotels - Save70.com'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward errorcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2360592: cache has only 0 modules [Step 1164] loss_orig = -0.351560, loss_refine = 0.197905[Step 1164] loss_orig = -0.342073, loss_refine = 0.197318[Step 1164] loss_orig = -0.351054, loss_refine = 1.756578 [Step 1164] loss_orig = -0.352233, loss_refine = -1.363679[Step 1164] loss_orig = -0.352720, loss_refine = 0.196005 [Step 1164] loss_orig = -0.352110, loss_refine = 0.195539 [Step 1164] loss_orig = -0.351657, loss_refine = -1.363332 [Step 1164] loss_orig = 2.481948, loss_refine = 0.195948 96%|█████████▋| 1165/1208 [14:55:01<31:42, 44.24s/it] {'loss': 0.0016, 'grad_norm': 6.367285174490358, 'learning_rate': 3.559602649006622e-08, 'completion_length': 100.20833333333333, 'rewards/accuracy_reward_action': 0.875, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.25, 'reward_std': 0.5671767095724741, 'kl': 0.06640625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.72} 96%|█████████▋| 1165/1208 [14:55:01<31:42, 44.24s/it]Start loss calc for inst: click the UI element Learn about third-party sign-in Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2361465: cache has only 0 modules Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2362338: cache has only 0 modules 97%|█████████▋| 1166/1208 [14:55:42<30:28, 43.54s/it] {'loss': 0.0026, 'grad_norm': 4.132358412300038, 'learning_rate': 3.47682119205298e-08, 'completion_length': 93.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0653076171875, 'clip_ratio': 0.0, 'epoch': 7.72} 97%|█████████▋| 1166/1208 [14:55:42<30:28, 43.54s/it]Start loss calc for inst: scan qr code Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2363211: cache has only 0 modules Start loss calc for inst: click the UI element + var indexRouter = require('./routes/index'); Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2364084: cache has only 0 modules 97%|█████████▋| 1167/1208 [14:56:37<31:59, 46.83s/it] {'loss': 0.0039, 'grad_norm': 6.55811794073059, 'learning_rate': 3.3940397350993376e-08, 'completion_length': 118.375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 0.9375, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0985107421875, 'clip_ratio': 0.0, 'epoch': 7.73} 97%|█████████▋| 1167/1208 [14:56:37<31:59, 46.83s/it]Start loss calc for inst: click the UI element Apple Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2364957: cache has only 0 modules Start loss calc for inst: click the UI element Convert to SmartArt Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2365830: cache has only 0 modules 97%|█████████▋| 1168/1208 [14:57:12<28:51, 43.30s/it] {'loss': 0.0013, 'grad_norm': 11.19307115525756, 'learning_rate': 3.311258278145695e-08, 'completion_length': 89.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0328369140625, 'clip_ratio': 0.0, 'epoch': 7.74} 97%|█████████▋| 1168/1208 [14:57:12<28:51, 43.30s/it]Start loss calc for inst: click the UI element Privacy Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2366703: cache has only 0 modules Start loss calc for inst: manage the outlayer Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2367576: cache has only 0 modules 97%|█████████▋| 1169/1208 [14:57:56<28:16, 43.50s/it] {'loss': 0.0021, 'grad_norm': 15.290884584147618, 'learning_rate': 3.228476821192053e-08, 'completion_length': 106.8125, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.4375, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.49871626496315, 'kl': 0.051513671875, 'clip_ratio': 0.0, 'epoch': 7.74} 97%|█████████▋| 1169/1208 [14:57:56<28:16, 43.50s/it]Start loss calc for inst: click the UI element Bing Real Estate - Home sales and rental listings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2368449: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Bing Real Estate - Home sales and rental listings'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2369322: cache has only 0 modules [Step 1169] loss_orig = 0.001332, loss_refine = 0.003921 [Step 1169] loss_orig = 0.003058, loss_refine = 0.003512 [Step 1169] loss_orig = 0.001326, loss_refine = -1.868693 [Step 1169] loss_orig = 0.002961, loss_refine = 0.001661 [Step 1169] loss_orig = 0.000843, loss_refine = 0.001345 [Step 1169] loss_orig = 0.001321, loss_refine = 1.872056 [Step 1169] loss_orig = 0.000911, loss_refine = 0.001178 [Step 1169] loss_orig = 0.001591, loss_refine = 0.001418 Start loss calc for inst: click the UI element Object... Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2370195: cache has only 0 modules 97%|█████████▋| 1170/1208 [14:59:00<31:21, 49.51s/it] {'loss': 0.0019, 'grad_norm': 16.888720112539062, 'learning_rate': 3.1456953642384106e-08, 'completion_length': 109.375, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.5833333333333335, 'reward_std': 0.41387641429901123, 'kl': 0.0416259765625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.875, 'epoch': 7.75} 97%|█████████▋| 1170/1208 [14:59:00<31:21, 49.51s/it]Start loss calc for inst: click the UI element Zoom out Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2371068: cache has only 0 modules Start loss calc for inst: click the UI element Today, 6:22 PM Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2371941: cache has only 0 modules 97%|█████████▋| 1171/1208 [14:59:42<29:14, 47.41s/it] {'loss': 0.0021, 'grad_norm': 7.793658001048843, 'learning_rate': 3.062913907284768e-08, 'completion_length': 93.0, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.05303955078125, 'clip_ratio': 0.0, 'epoch': 7.75} 97%|█████████▋| 1171/1208 [14:59:42<29:14, 47.41s/it]Start loss calc for inst: add new contact Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2372814: cache has only 0 modules Start loss calc for inst: click the UI element Social Integrations Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2373687: cache has only 0 modules 97%|█████████▋| 1172/1208 [15:00:20<26:40, 44.46s/it] {'loss': 0.0013, 'grad_norm': 0.22856423985695065, 'learning_rate': 2.9801324503311256e-08, 'completion_length': 83.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.03338623046875, 'clip_ratio': 0.0, 'epoch': 7.76} 97%|█████████▋| 1172/1208 [15:00:20<26:40, 44.46s/it]Start loss calc for inst: click the UI element amazon - Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2374560: cache has only 0 modules Start loss calc for inst: cancel the event Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2375433: cache has only 0 modules 97%|█████████▋| 1173/1208 [15:00:58<24:54, 42.69s/it] {'loss': 0.0011, 'grad_norm': 4.810143131214409, 'learning_rate': 2.8973509933774833e-08, 'completion_length': 95.5625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.02655029296875, 'clip_ratio': 0.0, 'epoch': 7.77} 97%|█████████▋| 1173/1208 [15:00:58<24:54, 42.69s/it]Start loss calc for inst: click the UI element Select language: current language is English Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2376306: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Select language: current language is English'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt boxcloser to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.5 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2377179: cache has only 0 modules [Step 1173] loss_orig = 0.001572, loss_refine = 0.937469 [Step 1173] loss_orig = 0.001988, loss_refine = 0.937388[Step 1173] loss_orig = 0.002229, loss_refine = 0.937404 [Step 1173] loss_orig = 0.001213, loss_refine = -0.933600 [Step 1173] loss_orig = 0.001916, loss_refine = 0.937867 [Step 1173] loss_orig = 0.001482, loss_refine = -0.933669 [Step 1173] loss_orig = 0.003434, loss_refine = -0.933856 [Step 1173] loss_orig = 0.002036, loss_refine = -0.933567 Start loss calc for inst: click the UI element Fit to page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2378052: cache has only 0 modules 97%|█████████▋| 1174/1208 [15:01:59<27:11, 47.99s/it] {'loss': 0.002, 'grad_norm': 8.012999324666172, 'learning_rate': 2.814569536423841e-08, 'completion_length': 104.79166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.2960252861181895, 'kl': 0.0516357421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.5, 'epoch': 7.77} 97%|█████████▋| 1174/1208 [15:01:59<27:11, 47.99s/it]Start loss calc for inst: click the UI element Collectibles Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2378925: cache has only 0 modules Start loss calc for inst: click the UI element Search Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2379798: cache has only 0 modules 97%|█████████▋| 1175/1208 [15:02:42<25:42, 46.75s/it] {'loss': 0.0014, 'grad_norm': 12.000799228666452, 'learning_rate': 2.7317880794701986e-08, 'completion_length': 90.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0347900390625, 'clip_ratio': 0.0, 'epoch': 7.78} 97%|█████████▋| 1175/1208 [15:02:42<25:42, 46.75s/it]Start loss calc for inst: click the UI element Pop-ups and redirects Block (default) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2380671: cache has only 0 modules Start loss calc for inst: timer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2381544: cache has only 0 modules 97%|█████████▋| 1176/1208 [15:03:24<24:09, 45.31s/it] {'loss': 0.0014, 'grad_norm': 6.337895710618988, 'learning_rate': 2.6490066225165563e-08, 'completion_length': 101.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0345458984375, 'clip_ratio': 0.0, 'epoch': 7.79} 97%|█████████▋| 1176/1208 [15:03:24<24:09, 45.31s/it]Start loss calc for inst: click the UI element Rectangle: Diagonal Corners Snipped 2 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2382417: cache has only 0 modules Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2383290: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [570, 79] }, {'action': 'click', 'coordinate': [507, 94] }, {'action': 'click', 'coordinate': [959, 82] }, {'action': 'click', 'coordinate': [1047, 86] }] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2384163: cache has only 0 modules [Step 1176] loss_orig = 0.001019, loss_refine = -1.205872 [Step 1176] loss_orig = 0.008233, loss_refine = -1.206279[Step 1176] loss_orig = 0.001758, loss_refine = 0.725849 [Step 1176] loss_orig = 0.002023, loss_refine = 0.725377 [Step 1176] loss_orig = 0.001715, loss_refine = -1.206565[Step 1176] loss_orig = 0.002620, loss_refine = 0.726191 [Step 1176] loss_orig = 0.001919, loss_refine = 0.726335[Step 1176] loss_orig = 0.002179, loss_refine = 0.725322 97%|█████████▋| 1177/1208 [15:04:25<25:50, 50.02s/it] {'loss': 0.0011, 'grad_norm': 7.334437347833586, 'learning_rate': 2.5662251655629136e-08, 'completion_length': 119.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.04443359375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.79} 97%|█████████▋| 1177/1208 [15:04:25<25:50, 50.02s/it]Start loss calc for inst: go to user account page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2385036: cache has only 0 modules Start loss calc for inst: click the UI element Top stories Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2385909: cache has only 0 modules 98%|█████████▊| 1178/1208 [15:05:02<23:01, 46.05s/it] {'loss': 0.0009, 'grad_norm': 3.140780794810104, 'learning_rate': 2.4834437086092713e-08, 'completion_length': 84.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02337646484375, 'clip_ratio': 0.0, 'epoch': 7.8} 98%|█████████▊| 1178/1208 [15:05:02<23:01, 46.05s/it]Start loss calc for inst: show all downloading apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2386782: cache has only 0 modules Start loss calc for inst: view the outdoor cycle report Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2387655: cache has only 0 modules 98%|█████████▊| 1179/1208 [15:05:40<21:05, 43.64s/it] {'loss': 0.0022, 'grad_norm': 21.514297401522615, 'learning_rate': 2.4006622516556293e-08, 'completion_length': 99.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.5625, 'rewards/format_reward': 1.0, 'reward': 2.5625, 'reward_std': 0.1767766922712326, 'kl': 0.0538330078125, 'clip_ratio': 0.0, 'epoch': 7.81} 98%|█████████▊| 1179/1208 [15:05:40<21:05, 43.64s/it]Start loss calc for inst: click the UI element Additional Information Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2388528: cache has only 0 modules Start loss calc for inst: click the UI element Class: MsoCommandBar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2389401: cache has only 0 modules 98%|█████████▊| 1180/1208 [15:06:22<20:07, 43.14s/it] {'loss': 0.0009, 'grad_norm': 5.162162391566554, 'learning_rate': 2.3178807947019866e-08, 'completion_length': 101.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.02130126953125, 'clip_ratio': 0.0, 'epoch': 7.81} 98%|█████████▊| 1180/1208 [15:06:22<20:07, 43.14s/it]Start loss calc for inst: click the UI element Deliver to Hong Kong Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2390274: cache has only 0 modules Start loss calc for inst: click the UI element Can't Undo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2391147: cache has only 0 modules 98%|█████████▊| 1181/1208 [15:07:12<20:18, 45.14s/it] {'loss': 0.0013, 'grad_norm': 5.419877817331338, 'learning_rate': 2.2350993377483443e-08, 'completion_length': 112.6875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.032135009765625, 'clip_ratio': 0.0, 'epoch': 7.82} 98%|█████████▊| 1181/1208 [15:07:12<20:18, 45.14s/it]Start loss calc for inst: click the UI element No Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2392020: cache has only 0 modules Start loss calc for inst: click the UI element Stickman Dragon Fight Stickman Dragon Fight Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2392893: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Stickman Dragon Fight Stickman Dragon Fight'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2393766: cache has only 0 modules [Step 1181] loss_orig = 0.001436, loss_refine = -1.206169[Step 1181] loss_orig = 0.001571, loss_refine = 0.726505[Step 1181] loss_orig = 0.000844, loss_refine = 0.725130[Step 1181] loss_orig = 0.002607, loss_refine = -1.205844 [Step 1181] loss_orig = 0.001337, loss_refine = 0.726157 [Step 1181] loss_orig = 0.000839, loss_refine = -1.206491 [Step 1181] loss_orig = 0.001522, loss_refine = 0.725766 [Step 1181] loss_orig = 0.001523, loss_refine = 0.725926 98%|█████████▊| 1182/1208 [15:08:19<22:23, 51.66s/it] {'loss': 0.0012, 'grad_norm': 12.52060014546347, 'learning_rate': 2.1523178807947016e-08, 'completion_length': 104.66666666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.4166666666666665, 'reward_std': 0.2903675138950348, 'kl': 0.03094482421875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.83} 98%|█████████▊| 1182/1208 [15:08:19<22:23, 51.66s/it]Start loss calc for inst: handwrite mode Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2394639: cache has only 0 modules Start loss calc for inst: click the UI element Sort Z to A Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2395512: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Sort Z to A'. Your previous answer was [{'action': 'click', 'coordinate': [832, 83]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt boxcloser to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2396385: cache has only 0 modules [Step 1182] loss_orig = 0.001245, loss_refine = -1.206463[Step 1182] loss_orig = 0.008817, loss_refine = 0.725586 [Step 1182] loss_orig = 0.004242, loss_refine = 0.725235[Step 1182] loss_orig = 0.000762, loss_refine = -1.206541[Step 1182] loss_orig = 0.000967, loss_refine = 0.725640 [Step 1182] loss_orig = 0.001436, loss_refine = 0.725381 [Step 1182] loss_orig = 0.002038, loss_refine = 0.725898 [Step 1182] loss_orig = 0.001282, loss_refine = -1.206203 98%|█████████▊| 1183/1208 [15:09:13<21:49, 52.36s/it] {'loss': 0.0017, 'grad_norm': 5.432752285001868, 'learning_rate': 2.0695364238410596e-08, 'completion_length': 103.29166666666667, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 1.0, 'reward': 2.4583333333333335, 'reward_std': 0.17251638571421304, 'kl': 0.060791015625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.83} 98%|█████████▊| 1183/1208 [15:09:13<21:49, 52.36s/it]Start loss calc for inst: click the UI element Fundraisers Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2397258: cache has only 0 modules Start loss calc for inst: click the UI element Page Number Page 1 of 1 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2398131: cache has only 0 modules 98%|█████████▊| 1184/1208 [15:09:52<19:23, 48.48s/it] {'loss': 0.0006, 'grad_norm': 7.982724561577915, 'learning_rate': 1.9867549668874173e-08, 'completion_length': 91.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.014678955078125, 'clip_ratio': 0.0, 'epoch': 7.84} 98%|█████████▊| 1184/1208 [15:09:52<19:23, 48.48s/it]Start loss calc for inst: click the UI element Conditional Formatting Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2399004: cache has only 0 modules Start loss calc for inst: open gmail Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2399877: cache has only 0 modules 98%|█████████▊| 1185/1208 [15:10:29<17:12, 44.88s/it] {'loss': 0.0027, 'grad_norm': 4.4819448123863905, 'learning_rate': 1.9039735099337746e-08, 'completion_length': 95.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 1.0, 'reward': 2.8125, 'reward_std': 0.2587745785713196, 'kl': 0.0673828125, 'clip_ratio': 0.0, 'epoch': 7.85} 98%|█████████▊| 1185/1208 [15:10:29<17:12, 44.88s/it]Start loss calc for inst: click the UI element 20240822_163021 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2400750: cache has only 0 modules Start loss calc for inst: return Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2401623: cache has only 0 modules 98%|█████████▊| 1186/1208 [15:11:05<15:28, 42.21s/it] {'loss': 0.0015, 'grad_norm': 7.5688582242758065, 'learning_rate': 1.8211920529801323e-08, 'completion_length': 93.0625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.03814697265625, 'clip_ratio': 0.0, 'epoch': 7.85} 98%|█████████▊| 1186/1208 [15:11:05<15:28, 42.21s/it]Start loss calc for inst: click the UI element YouTube Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2402496: cache has only 0 modules Start loss calc for inst: click the UI element From Text/CSV Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2403369: cache has only 0 modules 98%|█████████▊| 1187/1208 [15:11:45<14:37, 41.78s/it] {'loss': 0.001, 'grad_norm': 7.063889857064069, 'learning_rate': 1.73841059602649e-08, 'completion_length': 92.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0250244140625, 'clip_ratio': 0.0, 'epoch': 7.86} 98%|█████████▊| 1187/1208 [15:11:45<14:37, 41.78s/it]Start loss calc for inst: click the UI element Queries & Connections Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2404242: cache has only 0 modules Start loss calc for inst: click the UI element 945 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2405115: cache has only 0 modules 98%|█████████▊| 1188/1208 [15:12:29<14:06, 42.35s/it] {'loss': 0.0008, 'grad_norm': 4.557751481759857, 'learning_rate': 1.6556291390728476e-08, 'completion_length': 95.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.01953125, 'clip_ratio': 0.0, 'epoch': 7.87} 98%|█████████▊| 1188/1208 [15:12:29<14:06, 42.35s/it]Start loss calc for inst: click the UI element Comments Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2405988: cache has only 0 modules Start loss calc for inst: click the UI element slider pause button Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2406861: cache has only 0 modules 98%|█████████▊| 1189/1208 [15:13:11<13:21, 42.20s/it] {'loss': 0.0012, 'grad_norm': 0.3252120090175355, 'learning_rate': 1.5728476821192053e-08, 'completion_length': 100.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.029052734375, 'clip_ratio': 0.0, 'epoch': 7.87} 98%|█████████▊| 1189/1208 [15:13:11<13:21, 42.20s/it]Start loss calc for inst: click the UI element See more hotels Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2407734: cache has only 0 modules Start loss calc for inst: click the UI element Spelling and Grammar Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2408607: cache has only 0 modules 99%|█████████▊| 1190/1208 [15:13:51<12:29, 41.63s/it] {'loss': 0.0041, 'grad_norm': 5.946162662113604, 'learning_rate': 1.4900662251655628e-08, 'completion_length': 94.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.101318359375, 'clip_ratio': 0.0, 'epoch': 7.88} 99%|█████████▊| 1190/1208 [15:13:51<12:29, 41.63s/it]Start loss calc for inst: click the UI element LibreOffice Writer Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2409480: cache has only 0 modules Start loss calc for inst: click the UI element plateforme Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2410353: cache has only 0 modules 99%|█████████▊| 1191/1208 [15:14:38<12:11, 43.01s/it] {'loss': 0.002, 'grad_norm': 3.6283713770625496, 'learning_rate': 1.4072847682119205e-08, 'completion_length': 106.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.05029296875, 'clip_ratio': 0.0, 'epoch': 7.89} 99%|█████████▊| 1191/1208 [15:14:38<12:11, 43.01s/it]Start loss calc for inst:  battery options Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2411226: cache has only 0 modules Start loss calc for inst: click the UI element Wikipedia, the free encyclopedia Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2412099: cache has only 0 modules 99%|█████████▊| 1192/1208 [15:15:22<11:33, 43.35s/it] {'loss': 0.0019, 'grad_norm': 0.2535786798086201, 'learning_rate': 1.3245033112582781e-08, 'completion_length': 91.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0482177734375, 'clip_ratio': 0.0, 'epoch': 7.89} 99%|█████████▊| 1192/1208 [15:15:22<11:33, 43.35s/it]Start loss calc for inst: click the UI element Follow on Youtube Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2412972: cache has only 0 modules Start loss calc for inst: check device location Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2413845: cache has only 0 modules 99%|█████████▉| 1193/1208 [15:16:10<11:13, 44.88s/it] {'loss': 0.002, 'grad_norm': 16.15024591433088, 'learning_rate': 1.2417218543046356e-08, 'completion_length': 110.0625, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.8125, 'rewards/format_reward': 0.9375, 'reward': 2.6875, 'reward_std': 0.5303300619125366, 'kl': 0.0487060546875, 'clip_ratio': 0.0, 'epoch': 7.9} 99%|█████████▉| 1193/1208 [15:16:10<11:13, 44.88s/it]Start loss calc for inst: open settings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2414718: cache has only 0 modules Start loss calc for inst: click the UI element Face Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2415591: cache has only 0 modules 99%|█████████▉| 1194/1208 [15:16:46<09:51, 42.25s/it] {'loss': 0.002, 'grad_norm': 26.226720261396334, 'learning_rate': 1.1589403973509933e-08, 'completion_length': 87.9375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.049560546875, 'clip_ratio': 0.0, 'epoch': 7.91} 99%|█████████▉| 1194/1208 [15:16:46<09:51, 42.25s/it]Start loss calc for inst: click the UI element Zoom 376% Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2416464: cache has only 0 modules Start loss calc for inst: click the UI element 9. Cookies & similar technologies Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2417337: cache has only 0 modules 99%|█████████▉| 1195/1208 [15:17:27<09:02, 41.73s/it] {'loss': 0.002, 'grad_norm': 13.691871559343927, 'learning_rate': 1.0761589403973508e-08, 'completion_length': 100.875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.05072021484375, 'clip_ratio': 0.0, 'epoch': 7.91} 99%|█████████▉| 1195/1208 [15:17:27<09:02, 41.73s/it]Start loss calc for inst: click the UI element 4 Stars & Up& Up Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2418210: cache has only 0 modules Start loss calc for inst: click the UI element Follow on Twitter Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 0.875 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2419083: cache has only 0 modules 99%|█████████▉| 1196/1208 [15:18:21<09:05, 45.47s/it] {'loss': 0.0018, 'grad_norm': 8.340465156944596, 'learning_rate': 9.933774834437086e-09, 'completion_length': 115.0, 'rewards/accuracy_reward_action': 0.9375, 'rewards/accuracy_reward_coord': 0.6875, 'rewards/format_reward': 0.9375, 'reward': 2.5625, 'reward_std': 0.6943258494138718, 'kl': 0.04388427734375, 'clip_ratio': 0.0, 'epoch': 7.92} 99%|█████████▉| 1196/1208 [15:18:21<09:05, 45.47s/it]Start loss calc for inst: close clock at 6 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2419956: cache has only 0 modules Start loss calc for inst: favorite the music Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2420829: cache has only 0 modules 99%|█████████▉| 1197/1208 [15:18:58<07:50, 42.79s/it] {'loss': 0.0015, 'grad_norm': 0.4409979703274247, 'learning_rate': 9.105960264900661e-09, 'completion_length': 91.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 1.0, 'rewards/format_reward': 1.0, 'reward': 3.0, 'reward_std': 0.0, 'kl': 0.0380859375, 'clip_ratio': 0.0, 'epoch': 7.93} 99%|█████████▉| 1197/1208 [15:18:58<07:50, 42.79s/it]Start loss calc for inst: click the UI element Red Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2421702: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Red'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] diff coord reward error closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt boxcloser to gt box Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2422575: cache has only 0 modules [Step 1197] loss_orig = 0.001266, loss_refine = -1.321166[Step 1197] loss_orig = 0.002687, loss_refine = 1.324404 [Step 1197] loss_orig = 0.002255, loss_refine = 0.001533 [Step 1197] loss_orig = 0.001168, loss_refine = 0.001214[Step 1197] loss_orig = 0.001291, loss_refine = 0.001153 [Step 1197] loss_orig = 0.000776, loss_refine = 0.002438 [Step 1197] loss_orig = 0.001558, loss_refine = 1.326948 [Step 1197] loss_orig = 0.001219, loss_refine = -1.321373 Start loss calc for inst: display phone files Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2423448: cache has only 0 modules 99%|█████████▉| 1198/1208 [15:19:58<07:59, 47.97s/it] {'loss': 0.0028, 'grad_norm': 9.20628641817068, 'learning_rate': 8.278145695364238e-09, 'completion_length': 106.875, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.3333333333333333, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.2519763112068176, 'kl': 0.06494140625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.93} 99%|█████████▉| 1198/1208 [15:19:58<07:59, 47.97s/it]Start loss calc for inst: click the UI element Microsoft Edge - 1 running window Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2424321: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Microsoft Edge - 1 running window'. Your previous answer was [{'action': 'click', 'coordinate': [610, 1407]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box diff coord reward error Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2425194: cache has only 0 modules [Step 1198] loss_orig = 0.002932, loss_refine = -0.880770 [Step 1198] loss_orig = 0.001490, loss_refine = -0.881440[Step 1198] loss_orig = 0.002382, loss_refine = -0.880824 [Step 1198] loss_orig = 0.001587, loss_refine = 0.126997 [Step 1198] loss_orig = 0.001610, loss_refine = 0.127162 [Step 1198] loss_orig = 0.001614, loss_refine = 0.127553 [Step 1198] loss_orig = 0.001638, loss_refine = 0.126786 [Step 1198] loss_orig = 0.003163, loss_refine = 2.144754 Start loss calc for inst: click the UI element Allow Edit Ranges Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2426067: cache has only 0 modules 99%|█████████▉| 1199/1208 [15:21:13<08:26, 56.24s/it] {'loss': 0.0011, 'grad_norm': 15.824221387295903, 'learning_rate': 7.450331125827814e-09, 'completion_length': 122.29166666666667, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.4481948713461558, 'kl': 0.03814697265625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.94} 99%|█████████▉| 1199/1208 [15:21:13<08:26, 56.24s/it]Start loss calc for inst: click the UI element Skip to main content Reward function name: accuracy_reward_action Reward: 0.875 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 0.875 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2426940: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Skip to main content'. Your previous answer was [{'action': 'click', 'coordinate': [47, 61]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.375 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2427813: cache has only 0 modules [Step 1199] loss_orig = -0.351966, loss_refine = 0.725291 [Step 1199] loss_orig = -0.352630, loss_refine = 0.725561 [Step 1199] loss_orig = -0.352601, loss_refine = 0.728262 [Step 1199] loss_orig = -0.352261, loss_refine = -1.206435 [Step 1199] loss_orig = -0.352836, loss_refine = -1.206125 [Step 1199] loss_orig = -0.352378, loss_refine = 0.725314 [Step 1199] loss_orig = 2.475279, loss_refine = 0.725937 [Step 1199] loss_orig = -0.352657, loss_refine = -1.206173 Start loss calc for inst: create a new workbook for total a list Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2428686: cache has only 0 modules 99%|█████████▉| 1200/1208 [15:22:31<08:21, 62.73s/it] {'loss': 0.0019, 'grad_norm': 13.24151775083123, 'learning_rate': 6.622516556291391e-09, 'completion_length': 106.75, 'rewards/accuracy_reward_action': 0.9583333333333334, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 0.9583333333333334, 'reward': 2.3333333333333335, 'reward_std': 0.5260697702566782, 'kl': 0.040771484375, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.375, 'epoch': 7.95} 99%|█████████▉| 1200/1208 [15:22:31<08:21, 62.73s/it]Start loss calc for inst: add a new one /home/visitor_km/miniconda3/envs/ui-r1/lib/python3.10/site-packages/torch/utils/checkpoint.py:86: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2429559: cache has only 0 modules Start loss calc for inst: click the UI element Copilot (Ctrl+Shift+.) Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2430432: cache has only 0 modules 99%|█████████▉| 1201/1208 [15:23:25<07:00, 60.12s/it] {'loss': 0.003, 'grad_norm': 6.534210294983473, 'learning_rate': 5.7947019867549666e-09, 'completion_length': 91.75, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.9375, 'rewards/format_reward': 1.0, 'reward': 2.9375, 'reward_std': 0.1767766922712326, 'kl': 0.0753173828125, 'clip_ratio': 0.0, 'epoch': 7.95} 99%|█████████▉| 1201/1208 [15:23:25<07:00, 60.12s/it]Start loss calc for inst: click the UI element Text Highlight Color Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2431305: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'click the UI element Text Highlight Color'. Your previous answer was [{'action': 'click', 'coordinate': [540, 195]}] which is represented as a red box, with its center indicating the previously predicted coordinate. Your previous prediction was incorrect. Which direction (up, down, left, right, or different point) should the coordinate be adjusted to? You must clearly state adjustment direction and the reason within thinking process. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt boxcloser to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.25 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 0.25 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2432178: cache has only 0 modules [Step 1201] loss_orig = 0.001128, loss_refine = 0.541504[Step 1201] loss_orig = 0.001071, loss_refine = 0.541601[Step 1201] loss_orig = 0.001146, loss_refine = 0.541000[Step 1201] loss_orig = 0.000978, loss_refine = 0.541521 [Step 1201] loss_orig = 0.000909, loss_refine = 0.541095 [Step 1201] loss_orig = 0.002467, loss_refine = -1.618473 [Step 1201] loss_orig = 0.002105, loss_refine = -1.616414 [Step 1201] loss_orig = 0.001594, loss_refine = 0.541163 Start loss calc for inst: switch to song lyric Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.625 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2433051: cache has only 0 modules 100%|█████████▉| 1202/1208 [15:24:27<06:04, 60.70s/it] {'loss': 0.0019, 'grad_norm': 14.709086470708929, 'learning_rate': 4.966887417218543e-09, 'completion_length': 108.625, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.2916666666666667, 'rewards/format_reward': 1.0, 'reward': 2.375, 'reward_std': 0.48112308979034424, 'kl': 0.0457763671875, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 0.25, 'epoch': 7.96} 100%|█████████▉| 1202/1208 [15:24:27<06:04, 60.70s/it]Start loss calc for inst: view details Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2433924: cache has only 0 modules Start loss calc for inst: add a new page Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2434797: cache has only 0 modules 100%|█████████▉| 1203/1208 [15:25:08<04:33, 54.72s/it] {'loss': 0.0029, 'grad_norm': 13.015488483577458, 'learning_rate': 4.139072847682119e-09, 'completion_length': 99.8125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.07330322265625, 'clip_ratio': 0.0, 'epoch': 7.97} 100%|█████████▉| 1203/1208 [15:25:08<04:33, 54.72s/it]Start loss calc for inst: click the UI element 343 Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2435670: cache has only 0 modules Start loss calc for inst: click the UI element Dark Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.875 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2436543: cache has only 0 modules 100%|█████████▉| 1204/1208 [15:25:51<03:24, 51.15s/it] {'loss': 0.0019, 'grad_norm': 17.32489833022161, 'learning_rate': 3.3112582781456954e-09, 'completion_length': 108.4375, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.3535533845424652, 'kl': 0.0484619140625, 'clip_ratio': 0.0, 'epoch': 7.97} 100%|█████████▉| 1204/1208 [15:25:51<03:24, 51.15s/it]Start loss calc for inst: click the UI element Replace with Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.75 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2437416: cache has only 0 modules Start loss calc for inst: click the UI element Slide Notes Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2438289: cache has only 0 modules 100%|█████████▉| 1205/1208 [15:26:29<02:21, 47.18s/it] {'loss': 0.0014, 'grad_norm': 18.560592163462324, 'learning_rate': 2.4834437086092716e-09, 'completion_length': 86.1875, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.875, 'rewards/format_reward': 1.0, 'reward': 2.875, 'reward_std': 0.2314550280570984, 'kl': 0.0357666015625, 'clip_ratio': 0.0, 'epoch': 7.98} 100%|█████████▉| 1205/1208 [15:26:29<02:21, 47.18s/it]Start loss calc for inst: show all news&magzaines apps Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2439162: cache has only 0 modules Start loss calc for inst: display more functions Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.5 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2440035: cache has only 0 modules 100%|█████████▉| 1206/1208 [15:27:05<01:27, 43.89s/it] {'loss': 0.0022, 'grad_norm': 6.740862274681825, 'learning_rate': 1.6556291390728477e-09, 'completion_length': 91.125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.75, 'rewards/format_reward': 1.0, 'reward': 2.75, 'reward_std': 0.26726123690605164, 'kl': 0.0546875, 'clip_ratio': 0.0, 'epoch': 7.99} 100%|█████████▉| 1206/1208 [15:27:05<01:27, 43.89s/it]Start loss calc for inst: invert the lens Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2440908: cache has only 0 modules Start loss calc for inst: open photo Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.125 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2441781: cache has only 0 modules 100%|█████████▉| 1207/1208 [15:27:44<00:42, 42.51s/it] {'loss': 0.0013, 'grad_norm': 20.38418397857355, 'learning_rate': 8.278145695364238e-10, 'completion_length': 96.3125, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.125, 'rewards/format_reward': 1.0, 'reward': 2.125, 'reward_std': 0.3535533845424652, 'kl': 0.0335693359375, 'clip_ratio': 0.0, 'epoch': 7.99} 100%|█████████▉| 1207/1208 [15:27:44<00:42, 42.51s/it]Start loss calc for inst: show week steps recordings Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 1.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2442654: cache has only 0 modules Start loss calc for inst: set to biggest font size Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.0 Reward function name: format_reward Reward: 1.0 Need refine: tensor([True], device='cuda:0') Invalidate trace cache @ step 0 and module 2443527: cache has only 0 modules Refine prompt: In this UI screenshot, I want to perform the command 'set to biggest font size'. You must find the target point based solely on the information explicitly visible in the current screenshot. Please provide the action to perform (enumerate in ['click', 'open_app', 'scroll', 'navigate_back', 'input_text']) and the coordinate where the cursor is moved to (integer) if click is performed. Output the thinking process in and final answer in tags. The output answer format should be as follows: ... [{'action': enum['click', 'open_app', 'scroll', 'navigate_back', 'input_text'], 'coordinate': [x, y]}] Please strictly follow the format. Refine reward funcs: [, , , ] closer to gt boxcloser to gt box closer to gt box closer to gt boxcloser to gt box closer to gt box closer to gt box closer to gt box Reward function name: accuracy_reward_action Reward: 1.0 Reward function name: accuracy_reward_coord Reward: 0.1666666716337204 Reward function name: format_reward Reward: 1.0 Reward function name: diff_coord_reward Reward: 1.0 Need refine: tensor([False], device='cuda:0') Invalidate trace cache @ step 0 and module 2444400: cache has only 0 modules [Step 1207] loss_orig = 0.002766, loss_refine = 0.356901[Step 1207] loss_orig = 0.000352, loss_refine = 0.354230[Step 1207] loss_orig = 0.000451, loss_refine = 0.354649 [Step 1207] loss_orig = 0.000721, loss_refine = 0.355091[Step 1207] loss_orig = 0.001522, loss_refine = 0.364974 [Step 1207] loss_orig = 0.002627, loss_refine = 0.354179 [Step 1207] loss_orig = 0.003985, loss_refine = 0.354532[Step 1207] loss_orig = 0.001687, loss_refine = -2.473101 100%|██████████| 1208/1208 [15:28:41<00:00, 46.87s/it] {'loss': 0.0024, 'grad_norm': 4.747533712430528, 'learning_rate': 0.0, 'completion_length': 92.72222391764323, 'rewards/accuracy_reward_action': 1.0, 'rewards/accuracy_reward_coord': 0.3888888905445735, 'rewards/format_reward': 1.0, 'reward': 2.7222222487131753, 'reward_std': 0.11785112818082173, 'kl': 0.0504150390625, 'clip_ratio': 0.0, 'rewards/diff_coord_reward': 1.0, 'epoch': 8.0} 100%|██████████| 1208/1208 [15:28:41<00:00, 46.87s/it] {'train_runtime': 55734.7335, 'train_samples_per_second': 0.043, 'train_steps_per_second': 0.022, 'train_loss': 0.0018774937466422977, 'epoch': 8.0} 100%|██████████| 1208/1208 [15:28:54<00:00, 46.87s/it] 100%|██████████| 1208/1208 [15:28:54<00:00, 46.14s/it]