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+{"time":"2026-05-20T23:03:57.557061337Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:04:12.677244745Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:04:27.37477351Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:04:42.325860189Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-05-20T23:04:57.16469592Z","level":"INFO","msg":"filestream: sending request","total_files":4,"history_offset":1246,"history_lines":4,"events_offset":343,"events_lines":1,"console_offset":1275,"console_lines":6}
+{"time":"2026-05-20T23:04:57.311575939Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-05-20T23:05:12.16394075Z","level":"INFO","msg":"filestream: sending request","total_files":0,"uploaded_len":3}
+{"time":"2026-05-20T23:05:12.344441959Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:05:42.2950785Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:05:42.733849301Z","level":"INFO","msg":"filestream: sending request","total_files":0,"complete":true,"exit_code":0}
+{"time":"2026-05-20T23:05:42.910465673Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T23:05:42.910594557Z","level":"INFO","msg":"handler: closed"}
+{"time":"2026-05-20T23:05:42.910661638Z","level":"INFO","msg":"sender: closed"}
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diff --git a/checkpoints/dagger/2iter/wandb/debug.log b/checkpoints/dagger/2iter/wandb/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..3af46c072a77a7ccc239967a1b6c59560d16a076
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/debug.log
@@ -0,0 +1,58 @@
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Current SDK version is 0.27.0
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Configure stats pid to 3757791
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:setup_run_log_directory():723] Logging user logs to /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132/wandb/run-20260520_222141-vz6r3agr/logs/debug.log
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:setup_run_log_directory():724] Logging internal logs to /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132/wandb/run-20260520_222141-vz6r3agr/logs/debug-internal.log
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():851] calling init triggers
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():856] wandb.init called with sweep_config: {}
+config: {'dataset': {'repo_id': 'local/dagger_episodes', 'root': '/home/shadeform/Desktop/robot_learning/base_new_dataset_dagger_v2_676', 'episodes': None, 'image_transforms': {'enable': False, 'max_num_transforms': 3, 'random_order': False, 'same_cloth_color': False, 'tfs': {'color_jitter': {'weight': 1.5, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.7, 1.3], 'contrast': [0.7, 1.3], 'saturation': [0.4, 1.6], 'hue': [-0.5, 0.5]}}, 'grayscale': {'weight': 0.5, 'type': 'RandomGrayscale', 'kwargs': {'p': 1.0}}, 'shadow': {'weight': 0.8, 'type': 'RandomShadow', 'kwargs': {'num_shadows': [1, 2], 'intensity': [0.4, 0.7], 'blur_sigma': [4.0, 20.0]}}, 'highlight': {'weight': 0.5, 'type': 'RandomHighlight', 'kwargs': {'num_highlights': [0, 1], 'intensity': [0.2, 0.5], 'sigma': [20.0, 60.0]}}, 'gamma': {'weight': 0.7, 'type': 'RandomGamma', 'kwargs': {'gamma': [0.6, 1.4]}}, 'blur': {'weight': 0.0, 'type': 'GaussianBlur', 'kwargs': {'kernel_size': [3, 3], 'sigma': [0.1, 1.5]}}, 'jpeg': {'weight': 0.0, 'type': 'RandomJPEG', 'kwargs': {'quality': [60, 95]}}, 'crop': {'weight': 0.5, 'type': 'RandomCropPreserveSize', 'kwargs': {'scale': [0.97, 1.0], 'ratio': [0.99, 1.01]}}, 'affine': {'weight': 0.0, 'type': 'RandomAffine', 'kwargs': {'degrees': [-5.0, 5.0], 'translate': [0.05, 0.05]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}}}, 'revision': None, 'use_imagenet_stats': True, 'video_backend': 'pyav', 'return_uint8': False, 'streaming': False}, 'env': None, 'policy': {'type': 'diffusion', 'n_obs_steps': 2, 'input_features': {'observation.state': {'type': <FeatureType.STATE: 'STATE'>, 'shape': [6]}, 'observation.images.front': {'type': <FeatureType.VISUAL: 'VISUAL'>, 'shape': [3, 480, 640]}}, 'output_features': {'action': {'type': <FeatureType.ACTION: 'ACTION'>, 'shape': [6]}}, 'device': 'cuda', 'use_amp': False, 'use_peft': False, 'push_to_hub': False, 'repo_id': None, 'private': None, 'tags': None, 'license': None, 'pretrained_path': '/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter1_25000steps_20260520_173844/checkpoints/022000/pretrained_model', 'horizon': 16, 'n_action_steps': 8, 'normalization_mapping': {'VISUAL': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'STATE': <NormalizationMode.MIN_MAX: 'MIN_MAX'>, 'ACTION': <NormalizationMode.MIN_MAX: 'MIN_MAX'>}, 'drop_n_last_frames': 7, 'vision_backbone_type': 'dinov2', 'vision_encoder_name': 'facebook/dinov2-small', 'freeze_vision_encoder': True, 'vision_backbone': 'resnet18', 'resize_shape': [224, 224], 'crop_ratio': 1.0, 'crop_shape': None, 'crop_is_random': True, 'pretrained_backbone_weights': 'ResNet18_Weights.IMAGENET1K_V1', 'use_group_norm': False, 'spatial_softmax_num_keypoints': 32, 'vit_pool_type': 'spatial_softmax', 'vit_feature_dim': 512, 'use_separate_rgb_encoder_per_camera': True, 'down_dims': [512, 1024, 2048], 'kernel_size': 5, 'n_groups': 8, 'diffusion_step_embed_dim': 128, 'use_film_scale_modulation': True, 'noise_scheduler_type': 'DDPM', 'num_train_timesteps': 100, 'beta_schedule': 'squaredcos_cap_v2', 'beta_start': 0.0001, 'beta_end': 0.02, 'prediction_type': 'epsilon', 'clip_sample': True, 'clip_sample_range': 1.0, 'num_inference_steps': 100, 'compile_model': False, 'compile_mode': 'reduce-overhead', 'do_mask_loss_for_padding': False, 'optimizer_lr': 5e-05, 'optimizer_betas': [0.95, 0.999], 'optimizer_eps': 1e-08, 'optimizer_weight_decay': 1e-06, 'scheduler_name': 'cosine', 'scheduler_warmup_steps': 100}, 'reward_model': None, 'output_dir': '/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132', 'job_name': 'diffusion_dagger_iter2_25000steps_20260520_222132', 'resume': False, 'seed': 1000, 'cudnn_deterministic': False, 'num_workers': 16, 'batch_size': 64, 'prefetch_factor': 4, 'persistent_workers': True, 'steps': 25000, 'eval_freq': 0, 'log_freq': 20, 'tolerance_s': 0.0001, 'save_checkpoint': True, 'save_freq': 2000, 'use_policy_training_preset': True, 'optimizer': {'type': 'adam', 'lr': 5e-05, 'weight_decay': 1e-06, 'grad_clip_norm': 10.0, 'betas': [0.95, 0.999], 'eps': 1e-08}, 'scheduler': {'type': 'diffuser', 'num_warmup_steps': 100, 'name': 'cosine'}, 'eval': {'n_episodes': 50, 'batch_size': 19, 'use_async_envs': True}, 'wandb': {'enable': True, 'disable_artifact': False, 'project': 'lerobot', 'entity': None, 'notes': None, 'run_id': None, 'mode': None, 'add_tags': True}, 'peft': None, 'sample_weighting': None, 'rename_map': {}, 'checkpoint_path': None, '_wandb': {}}
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():899] starting backend
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+2026-05-20 22:21:42,107 INFO    MainThread:3757791 [wandb_init.py:init():1057] starting run threads in backend
+2026-05-20 22:21:42,160 INFO    MainThread:3757791 [wandb_run.py:_console_start():2509] atexit reg
+2026-05-20 22:21:42,161 INFO    MainThread:3757791 [wandb_run.py:_redirect():2359] redirect: wrap_raw
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+2026-05-20 22:21:42,162 INFO    MainThread:3757791 [wandb_init.py:init():1095] run started, returning control to user process
+2026-05-20 23:04:55,815 INFO    wandb-AsyncioManager-main:3757791 [service_client.py:_forward_responses():122] Reached EOF.
+2026-05-20 23:04:55,815 INFO    wandb-AsyncioManager-main:3757791 [mailbox.py:close():154] Closing mailbox, abandoning 2 handles.
+2026-05-20 23:04:58,253 ERROR   wandb-AsyncioManager-main:3757791 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_stopping.py", line 87, in loop
+    should_stop = await self._check_should_stop()
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_stopping.py", line 115, in _check_should_stop
+    handle = await self._interface.deliver_async(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 58, in deliver_async
+    handle = await self._client.deliver(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/service/service_client.py", line 57, in deliver
+    handle = self._mailbox.require_response(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/mailbox/mailbox.py", line 89, in require_response
+    raise MailboxClosedError()
+wandb.sdk.mailbox.mailbox.MailboxClosedError
+2026-05-20 23:05:04,106 ERROR   wandb-AsyncioManager-main:3757791 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_messages.py", line 105, in loop
+    await self._poll_and_print(timeout=None)
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_messages.py", line 133, in _poll_and_print
+    handle = await self._interface.deliver_async(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 58, in deliver_async
+    handle = await self._client.deliver(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/service/service_client.py", line 57, in deliver
+    handle = self._mailbox.require_response(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/mailbox/mailbox.py", line 89, in require_response
+    raise MailboxClosedError()
+wandb.sdk.mailbox.mailbox.MailboxClosedError
diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/config.yaml b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fffe8b12fa0d8adb29691b6357798d94d92e57f7
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/config.yaml
@@ -0,0 +1,361 @@
+_wandb:
+    value:
+        cli_version: 0.27.0
+        e:
+            9urge5hgiybf40nn3p1bxxichewkwn39:
+                args:
+                    - --policy.path=/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter1_25000steps_20260520_173844/checkpoints/022000/pretrained_model/
+                    - --dataset.repo_id=local/dagger_episodes
+                    - --dataset.root=/home/shadeform/Desktop/robot_learning/base_new_dataset_dagger_v2_676
+                    - --dataset.video_backend=pyav
+                    - --policy.device=cuda
+                    - --policy.push_to_hub=false
+                    - --policy.n_obs_steps=2
+                    - --policy.horizon=16
+                    - --policy.n_action_steps=8
+                    - --policy.vision_backbone_type=dinov2
+                    - --policy.vision_backbone=resnet18
+                    - --policy.freeze_vision_encoder=true
+                    - --policy.noise_scheduler_type=DDPM
+                    - --policy.num_train_timesteps=100
+                    - --policy.num_inference_steps=100
+                    - --policy.optimizer_lr=5e-5
+                    - --policy.scheduler_warmup_steps=100
+                    - --output_dir=/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132
+                    - --job_name=diffusion_dagger_iter2_25000steps_20260520_222132
+                    - --batch_size=64
+                    - --steps=25000
+                    - --num_workers=16
+                    - --log_freq=20
+                    - --save_freq=2000
+                    - --eval_freq=0
+                    - --wandb.enable=True
+                    - --policy.vision_encoder_name=facebook/dinov2-small
+                    - --policy.resize_shape=[224,224]
+                codePath: .venv/bin/lerobot-train
+                codePathLocal: .venv/bin/lerobot-train
+                cpu_count: 28
+                cpu_count_logical: 28
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "103865303040"
+                        used: "51379896320"
+                executable: /home/shadeform/Desktop/robot_learning/lerobot/.venv/bin/python3
+                git:
+                    commit: e05f33153eb69762cfab647e20a1f1a9bce051ba
+                    remote: https://github.com/michavol/lerobot.git
+                gpu: NVIDIA H100 PCIe
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Hopper
+                      cudaCores: 14592
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 PCIe
+                      uuid: GPU-ce315b98-20ff-34fd-307b-fe05646f5913
+                host: brev-baxch8nuw
+                memory:
+                    total: "190128123904"
+                os: Linux-6.8.0-90-generic-x86_64-with-glibc2.35
+                program: /home/shadeform/Desktop/robot_learning/lerobot/.venv/bin/lerobot-train
+                python: CPython 3.12.13
+                root: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132
+                startedAt: "2026-05-20T22:21:41.390284Z"
+                writerId: 9urge5hgiybf40nn3p1bxxichewkwn39
+        m: []
+        python_version: 3.12.13
+        t:
+            "1":
+                - 1
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+                - 41
+                - 49
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+                - 71
+                - 83
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+                - 83
+            "3":
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+                - 15
+                - 16
+                - 61
+            "4": 3.12.13
+            "5": 0.27.0
+            "6": 5.5.4
+            "10":
+                - 21
+            "12": 0.27.0
+            "13": linux-x86_64
+batch_size:
+    value: 64
+checkpoint_path:
+    value: null
+cudnn_deterministic:
+    value: false
+dataset:
+    value:
+        episodes: null
+        image_transforms:
+            enable: false
+            max_num_transforms: 3
+            random_order: false
+            same_cloth_color: false
+            tfs:
+                affine:
+                    kwargs:
+                        degrees:
+                            - -5
+                            - 5
+                        translate:
+                            - 0.05
+                            - 0.05
+                    type: RandomAffine
+                    weight: 0
+                blur:
+                    kwargs:
+                        kernel_size:
+                            - 3
+                            - 3
+                        sigma:
+                            - 0.1
+                            - 1.5
+                    type: GaussianBlur
+                    weight: 0
+                color_jitter:
+                    kwargs:
+                        brightness:
+                            - 0.7
+                            - 1.3
+                        contrast:
+                            - 0.7
+                            - 1.3
+                        hue:
+                            - -0.5
+                            - 0.5
+                        saturation:
+                            - 0.4
+                            - 1.6
+                    type: ColorJitter
+                    weight: 1.5
+                crop:
+                    kwargs:
+                        ratio:
+                            - 0.99
+                            - 1.01
+                        scale:
+                            - 0.97
+                            - 1
+                    type: RandomCropPreserveSize
+                    weight: 0.5
+                gamma:
+                    kwargs:
+                        gamma:
+                            - 0.6
+                            - 1.4
+                    type: RandomGamma
+                    weight: 0.7
+                grayscale:
+                    kwargs:
+                        p: 1
+                    type: RandomGrayscale
+                    weight: 0.5
+                highlight:
+                    kwargs:
+                        intensity:
+                            - 0.2
+                            - 0.5
+                        num_highlights:
+                            - 0
+                            - 1
+                        sigma:
+                            - 20
+                            - 60
+                    type: RandomHighlight
+                    weight: 0.5
+                jpeg:
+                    kwargs:
+                        quality:
+                            - 60
+                            - 95
+                    type: RandomJPEG
+                    weight: 0
+                shadow:
+                    kwargs:
+                        blur_sigma:
+                            - 4
+                            - 20
+                        intensity:
+                            - 0.4
+                            - 0.7
+                        num_shadows:
+                            - 1
+                            - 2
+                    type: RandomShadow
+                    weight: 0.8
+                sharpness:
+                    kwargs:
+                        sharpness:
+                            - 0.5
+                            - 1.5
+                    type: SharpnessJitter
+                    weight: 1
+        repo_id: local/dagger_episodes
+        return_uint8: false
+        revision: null
+        root: /home/shadeform/Desktop/robot_learning/base_new_dataset_dagger_v2_676
+        streaming: false
+        use_imagenet_stats: true
+        video_backend: pyav
+env:
+    value: null
+eval:
+    value:
+        batch_size: 19
+        n_episodes: 50
+        use_async_envs: true
+eval_freq:
+    value: 0
+job_name:
+    value: diffusion_dagger_iter2_25000steps_20260520_222132
+log_freq:
+    value: 20
+num_workers:
+    value: 16
+optimizer:
+    value:
+        betas:
+            - 0.95
+            - 0.999
+        eps: 1e-08
+        grad_clip_norm: 10
+        lr: 5e-05
+        type: adam
+        weight_decay: 1e-06
+output_dir:
+    value: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132
+peft:
+    value: null
+persistent_workers:
+    value: true
+policy:
+    value:
+        beta_end: 0.02
+        beta_schedule: squaredcos_cap_v2
+        beta_start: 0.0001
+        clip_sample: true
+        clip_sample_range: 1
+        compile_mode: reduce-overhead
+        compile_model: false
+        crop_is_random: true
+        crop_ratio: 1
+        crop_shape: null
+        device: cuda
+        diffusion_step_embed_dim: 128
+        do_mask_loss_for_padding: false
+        down_dims:
+            - 512
+            - 1024
+            - 2048
+        drop_n_last_frames: 7
+        freeze_vision_encoder: true
+        horizon: 16
+        input_features:
+            observation.images.front:
+                shape:
+                    - 3
+                    - 480
+                    - 640
+                type: VISUAL
+            observation.state:
+                shape:
+                    - 6
+                type: STATE
+        kernel_size: 5
+        license: null
+        n_action_steps: 8
+        n_groups: 8
+        n_obs_steps: 2
+        noise_scheduler_type: DDPM
+        normalization_mapping:
+            ACTION: MIN_MAX
+            STATE: MIN_MAX
+            VISUAL: MEAN_STD
+        num_inference_steps: 100
+        num_train_timesteps: 100
+        optimizer_betas:
+            - 0.95
+            - 0.999
+        optimizer_eps: 1e-08
+        optimizer_lr: 5e-05
+        optimizer_weight_decay: 1e-06
+        output_features:
+            action:
+                shape:
+                    - 6
+                type: ACTION
+        prediction_type: epsilon
+        pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+        pretrained_path: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter1_25000steps_20260520_173844/checkpoints/022000/pretrained_model
+        private: null
+        push_to_hub: false
+        repo_id: null
+        resize_shape:
+            - 224
+            - 224
+        scheduler_name: cosine
+        scheduler_warmup_steps: 100
+        spatial_softmax_num_keypoints: 32
+        tags: null
+        type: diffusion
+        use_amp: false
+        use_film_scale_modulation: true
+        use_group_norm: false
+        use_peft: false
+        use_separate_rgb_encoder_per_camera: true
+        vision_backbone: resnet18
+        vision_backbone_type: dinov2
+        vision_encoder_name: facebook/dinov2-small
+        vit_feature_dim: 512
+        vit_pool_type: spatial_softmax
+prefetch_factor:
+    value: 4
+resume:
+    value: false
+reward_model:
+    value: null
+sample_weighting:
+    value: null
+save_checkpoint:
+    value: true
+save_freq:
+    value: 2000
+scheduler:
+    value:
+        name: cosine
+        num_warmup_steps: 100
+        type: diffuser
+seed:
+    value: 1000
+steps:
+    value: 25000
+tolerance_s:
+    value: 0.0001
+use_policy_training_preset:
+    value: true
+wandb:
+    value:
+        add_tags: true
+        disable_artifact: false
+        enable: true
+        entity: null
+        mode: null
+        notes: null
+        project: lerobot
+        run_id: null
diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/output.log b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..758450f8000256dc116f1179f6109d4cf550e2d1
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/output.log
@@ -0,0 +1,1281 @@
+INFO 2026-05-20 22:21:42 db_utils.py:121 Logs will be synced with wandb.
+INFO 2026-05-20 22:21:42 db_utils.py:122 Track this run --> https://wandb.ai/romainguntz-eth-z-rich/lerobot/runs/vz6r3agr
+INFO 2026-05-20 22:21:42 ot_train.py:236 Creating dataset
+INFO 2026-05-20 22:21:42 eo_utils.py:110 Using video codec: libsvtav1
+INFO 2026-05-20 22:21:42 ot_train.py:270 Creating policy
+Loading weights: 100%|███████████████████████████████████| 223/223 [00:00<00:00, 8949.41it/s]
+Loading weights from local directory
+INFO 2026-05-20 22:21:44 ot_train.py:347 Creating optimizer and scheduler
+INFO 2026-05-20 22:21:44 ot_train.py:374 Output dir: /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132
+INFO 2026-05-20 22:21:44 ot_train.py:381 cfg.steps=25000 (25K)
+INFO 2026-05-20 22:21:44 ot_train.py:382 dataset.num_frames=272705 (273K)
+INFO 2026-05-20 22:21:44 ot_train.py:383 dataset.num_episodes=676
+INFO 2026-05-20 22:21:44 ot_train.py:386 Effective batch size: 64 x 1 = 64
+INFO 2026-05-20 22:21:44 ot_train.py:387 num_learnable_params=251772134 (252M)
+INFO 2026-05-20 22:21:44 ot_train.py:388 num_total_params=273828710 (274M)
+Training:   0%|                                                  | 0/25000 [00:00<?, ?step/s]INFO 2026-05-20 22:21:44 ot_train.py:453 Start offline training on a fixed dataset, with effective batch size: 64
+Training:   0%|                                         | 20/25000 [00:12<52:42,  7.90step/s]INFO 2026-05-20 22:21:57 ot_train.py:488 step:20 smpl:1K ep:3 epch:0.00 loss:0.008 grdn:0.194 lr:5.3e-06 updt_s:0.371 data_s:0.275
+Training:   0%|                                         | 39/25000 [00:14<43:42,  9.52step/s]INFO 2026-05-20 22:21:59 ot_train.py:488 step:40 smpl:3K ep:6 epch:0.01 loss:0.008 grdn:0.182 lr:1.5e-05 updt_s:0.095 data_s:0.009
+Training:   0%|                                         | 59/25000 [00:16<40:21, 10.30step/s]INFO 2026-05-20 22:22:01 ot_train.py:488 step:60 smpl:4K ep:10 epch:0.01 loss:0.007 grdn:0.176 lr:2.5e-05 updt_s:0.092 data_s:0.006
+Training:   0%|▏                                        | 79/25000 [00:18<39:17, 10.57step/s]INFO 2026-05-20 22:22:03 ot_train.py:488 step:80 smpl:5K ep:13 epch:0.02 loss:0.009 grdn:0.204 lr:3.5e-05 updt_s:0.087 data_s:0.008
+Training:   0%|▏                                        | 99/25000 [00:20<41:44,  9.94step/s]INFO 2026-05-20 22:22:05 ot_train.py:488 step:100 smpl:6K ep:16 epch:0.02 loss:0.009 grdn:0.213 lr:4.5e-05 updt_s:0.088 data_s:0.013
+Training:   0%|▏                                       | 119/25000 [00:22<40:06, 10.34step/s]INFO 2026-05-20 22:22:07 ot_train.py:488 step:120 smpl:8K ep:19 epch:0.03 loss:0.009 grdn:0.216 lr:5.0e-05 updt_s:0.088 data_s:0.007
+Training:   1%|▏                                       | 140/25000 [00:24<40:07, 10.32step/s]INFO 2026-05-20 22:22:09 ot_train.py:488 step:140 smpl:9K ep:22 epch:0.03 loss:0.009 grdn:0.201 lr:5.0e-05 updt_s:0.088 data_s:0.011
+Training:   1%|▎                                       | 160/25000 [00:26<41:56,  9.87step/s]INFO 2026-05-20 22:22:11 ot_train.py:488 step:160 smpl:10K ep:25 epch:0.04 loss:0.008 grdn:0.194 lr:5.0e-05 updt_s:0.092 data_s:0.006
+Training:   1%|▎                                       | 179/25000 [00:28<38:27, 10.75step/s]INFO 2026-05-20 22:22:13 ot_train.py:488 step:180 smpl:12K ep:29 epch:0.04 loss:0.008 grdn:0.181 lr:5.0e-05 updt_s:0.091 data_s:0.008
+Training:   1%|▎                                       | 199/25000 [00:30<42:16,  9.78step/s]INFO 2026-05-20 22:22:15 ot_train.py:488 step:200 smpl:13K ep:32 epch:0.05 loss:0.009 grdn:0.191 lr:5.0e-05 updt_s:0.089 data_s:0.010
+Training:   1%|▎                                       | 220/25000 [00:32<36:48, 11.22step/s]INFO 2026-05-20 22:22:17 ot_train.py:488 step:220 smpl:14K ep:35 epch:0.05 loss:0.009 grdn:0.181 lr:5.0e-05 updt_s:0.084 data_s:0.008
+Training:   1%|▍                                       | 240/25000 [00:35<42:56,  9.61step/s]INFO 2026-05-20 22:22:19 ot_train.py:488 step:240 smpl:15K ep:38 epch:0.06 loss:0.008 grdn:0.176 lr:5.0e-05 updt_s:0.093 data_s:0.012
+Training:   1%|▍                                       | 259/25000 [00:36<40:46, 10.11step/s]INFO 2026-05-20 22:22:21 ot_train.py:488 step:260 smpl:17K ep:41 epch:0.06 loss:0.009 grdn:0.187 lr:5.0e-05 updt_s:0.086 data_s:0.010
+Training:   1%|▍                                       | 279/25000 [00:38<43:35,  9.45step/s]INFO 2026-05-20 22:22:23 ot_train.py:488 step:280 smpl:18K ep:44 epch:0.07 loss:0.008 grdn:0.181 lr:5.0e-05 updt_s:0.091 data_s:0.008
+Training:   1%|▍                                       | 300/25000 [00:40<41:47,  9.85step/s]INFO 2026-05-20 22:22:25 ot_train.py:488 step:300 smpl:19K ep:48 epch:0.07 loss:0.010 grdn:0.206 lr:5.0e-05 updt_s:0.085 data_s:0.015
+Training:   1%|▌                                       | 320/25000 [00:42<42:15,  9.73step/s]INFO 2026-05-20 22:22:27 ot_train.py:488 step:320 smpl:20K ep:51 epch:0.08 loss:0.009 grdn:0.206 lr:5.0e-05 updt_s:0.087 data_s:0.010
+Training:   1%|▌                                       | 340/25000 [00:44<38:29, 10.68step/s]INFO 2026-05-20 22:22:29 ot_train.py:488 step:340 smpl:22K ep:54 epch:0.08 loss:0.008 grdn:0.192 lr:5.0e-05 updt_s:0.087 data_s:0.011
+Training:   1%|▌                                       | 360/25000 [00:46<44:05,  9.31step/s]INFO 2026-05-20 22:22:31 ot_train.py:488 step:360 smpl:23K ep:57 epch:0.08 loss:0.009 grdn:0.204 lr:5.0e-05 updt_s:0.088 data_s:0.012
+Training:   2%|▌                                       | 380/25000 [00:48<37:17, 11.00step/s]INFO 2026-05-20 22:22:33 ot_train.py:488 step:380 smpl:24K ep:60 epch:0.09 loss:0.008 grdn:0.174 lr:5.0e-05 updt_s:0.084 data_s:0.011
+Training:   2%|▋                                       | 399/25000 [00:50<39:50, 10.29step/s]INFO 2026-05-20 22:22:35 ot_train.py:488 step:400 smpl:26K ep:63 epch:0.09 loss:0.009 grdn:0.178 lr:5.0e-05 updt_s:0.088 data_s:0.015
+Training:   2%|▋                                       | 420/25000 [00:52<41:03,  9.98step/s]INFO 2026-05-20 22:22:37 ot_train.py:488 step:420 smpl:27K ep:67 epch:0.10 loss:0.009 grdn:0.181 lr:5.0e-05 updt_s:0.091 data_s:0.008
+Training:   2%|▋                                       | 439/25000 [00:54<43:13,  9.47step/s]INFO 2026-05-20 22:22:39 ot_train.py:488 step:440 smpl:28K ep:70 epch:0.10 loss:0.009 grdn:0.196 lr:5.0e-05 updt_s:0.089 data_s:0.007
+Training:   2%|▋                                       | 459/25000 [00:56<40:17, 10.15step/s]INFO 2026-05-20 22:22:41 ot_train.py:488 step:460 smpl:29K ep:73 epch:0.11 loss:0.008 grdn:0.183 lr:5.0e-05 updt_s:0.088 data_s:0.011
+Training:   2%|▊                                       | 480/25000 [00:58<38:43, 10.55step/s]INFO 2026-05-20 22:22:43 ot_train.py:488 step:480 smpl:31K ep:76 epch:0.11 loss:0.008 grdn:0.178 lr:5.0e-05 updt_s:0.091 data_s:0.008
+Training:   2%|▊                                       | 499/25000 [01:00<39:10, 10.42step/s]INFO 2026-05-20 22:22:45 ot_train.py:488 step:500 smpl:32K ep:79 epch:0.12 loss:0.009 grdn:0.196 lr:5.0e-05 updt_s:0.089 data_s:0.012
+Training:   2%|▊                                       | 519/25000 [01:02<38:01, 10.73step/s]INFO 2026-05-20 22:22:47 ot_train.py:488 step:520 smpl:33K ep:82 epch:0.12 loss:0.010 grdn:0.223 lr:5.0e-05 updt_s:0.090 data_s:0.006
+Training:   2%|▊                                       | 540/25000 [01:04<38:00, 10.73step/s]INFO 2026-05-20 22:22:49 ot_train.py:488 step:540 smpl:35K ep:86 epch:0.13 loss:0.009 grdn:0.203 lr:5.0e-05 updt_s:0.090 data_s:0.006
+Training:   2%|▉                                       | 560/25000 [01:06<44:26,  9.16step/s]INFO 2026-05-20 22:22:51 ot_train.py:488 step:560 smpl:36K ep:89 epch:0.13 loss:0.010 grdn:0.212 lr:5.0e-05 updt_s:0.088 data_s:0.013
+Training:   2%|▉                                       | 580/25000 [01:08<38:07, 10.68step/s]INFO 2026-05-20 22:22:53 ot_train.py:488 step:580 smpl:37K ep:92 epch:0.14 loss:0.009 grdn:0.179 lr:5.0e-05 updt_s:0.087 data_s:0.006
+Training:   2%|▉                                       | 600/25000 [01:10<41:28,  9.80step/s]INFO 2026-05-20 22:22:55 ot_train.py:488 step:600 smpl:38K ep:95 epch:0.14 loss:0.008 grdn:0.171 lr:5.0e-05 updt_s:0.088 data_s:0.014
+Training:   2%|▉                                       | 619/25000 [01:12<37:34, 10.82step/s]INFO 2026-05-20 22:22:57 ot_train.py:488 step:620 smpl:40K ep:98 epch:0.15 loss:0.008 grdn:0.168 lr:5.0e-05 updt_s:0.088 data_s:0.008
+Training:   3%|█                                       | 639/25000 [01:14<40:43,  9.97step/s]INFO 2026-05-20 22:22:59 ot_train.py:488 step:640 smpl:41K ep:102 epch:0.15 loss:0.009 grdn:0.174 lr:5.0e-05 updt_s:0.090 data_s:0.010
+Training:   3%|█                                       | 659/25000 [01:16<39:47, 10.19step/s]INFO 2026-05-20 22:23:01 ot_train.py:488 step:660 smpl:42K ep:105 epch:0.15 loss:0.009 grdn:0.176 lr:5.0e-05 updt_s:0.086 data_s:0.008
+Training:   3%|█                                       | 680/25000 [01:18<44:48,  9.05step/s]INFO 2026-05-20 22:23:03 ot_train.py:488 step:680 smpl:44K ep:108 epch:0.16 loss:0.009 grdn:0.186 lr:5.0e-05 updt_s:0.089 data_s:0.013
+Training:   3%|█                                       | 699/25000 [01:20<38:36, 10.49step/s]INFO 2026-05-20 22:23:04 ot_train.py:488 step:700 smpl:45K ep:111 epch:0.16 loss:0.009 grdn:0.180 lr:5.0e-05 updt_s:0.083 data_s:0.010
+Training:   3%|█▏                                      | 719/25000 [01:22<40:25, 10.01step/s]INFO 2026-05-20 22:23:06 ot_train.py:488 step:720 smpl:46K ep:114 epch:0.17 loss:0.009 grdn:0.187 lr:5.0e-05 updt_s:0.089 data_s:0.010
+Training:   3%|█▏                                      | 739/25000 [01:24<39:36, 10.21step/s]INFO 2026-05-20 22:23:08 ot_train.py:488 step:740 smpl:47K ep:117 epch:0.17 loss:0.007 grdn:0.156 lr:5.0e-05 updt_s:0.086 data_s:0.012
+Training:   3%|█▏                                      | 759/25000 [01:26<38:11, 10.58step/s]INFO 2026-05-20 22:23:11 ot_train.py:488 step:760 smpl:49K ep:121 epch:0.18 loss:0.008 grdn:0.176 lr:5.0e-05 updt_s:0.093 data_s:0.009
+Training:   3%|█▏                                      | 779/25000 [01:28<38:36, 10.46step/s]INFO 2026-05-20 22:23:12 ot_train.py:488 step:780 smpl:50K ep:124 epch:0.18 loss:0.008 grdn:0.173 lr:5.0e-05 updt_s:0.087 data_s:0.009
+Training:   3%|█▎                                      | 799/25000 [01:30<39:38, 10.17step/s]INFO 2026-05-20 22:23:14 ot_train.py:488 step:800 smpl:51K ep:127 epch:0.19 loss:0.009 grdn:0.168 lr:5.0e-05 updt_s:0.091 data_s:0.007
+Training:   3%|█▎                                      | 819/25000 [01:32<40:04, 10.06step/s]INFO 2026-05-20 22:23:16 ot_train.py:488 step:820 smpl:52K ep:130 epch:0.19 loss:0.009 grdn:0.167 lr:5.0e-05 updt_s:0.089 data_s:0.010
+Training:   3%|█▎                                      | 839/25000 [01:34<39:02, 10.31step/s]INFO 2026-05-20 22:23:18 ot_train.py:488 step:840 smpl:54K ep:133 epch:0.20 loss:0.008 grdn:0.166 lr:5.0e-05 updt_s:0.089 data_s:0.008
+Training:   3%|█▎                                      | 859/25000 [01:36<39:36, 10.16step/s]INFO 2026-05-20 22:23:20 ot_train.py:488 step:860 smpl:55K ep:136 epch:0.20 loss:0.009 grdn:0.181 lr:5.0e-05 updt_s:0.088 data_s:0.011
+Training:   4%|█▍                                      | 879/25000 [01:38<39:16, 10.24step/s]INFO 2026-05-20 22:23:22 ot_train.py:488 step:880 smpl:56K ep:140 epch:0.21 loss:0.010 grdn:0.203 lr:5.0e-05 updt_s:0.093 data_s:0.007
+Training:   4%|█▍                                      | 899/25000 [01:40<41:30,  9.68step/s]INFO 2026-05-20 22:23:24 ot_train.py:488 step:900 smpl:58K ep:143 epch:0.21 loss:0.008 grdn:0.173 lr:5.0e-05 updt_s:0.087 data_s:0.010
+Training:   4%|█▍                                      | 920/25000 [01:42<43:09,  9.30step/s]INFO 2026-05-20 22:23:26 ot_train.py:488 step:920 smpl:59K ep:146 epch:0.22 loss:0.008 grdn:0.171 lr:5.0e-05 updt_s:0.094 data_s:0.006
+Training:   4%|█▌                                      | 939/25000 [01:44<37:28, 10.70step/s]INFO 2026-05-20 22:23:28 ot_train.py:488 step:940 smpl:60K ep:149 epch:0.22 loss:0.009 grdn:0.176 lr:5.0e-05 updt_s:0.088 data_s:0.007
+Training:   4%|█▌                                      | 959/25000 [01:46<40:43,  9.84step/s]INFO 2026-05-20 22:23:30 ot_train.py:488 step:960 smpl:61K ep:152 epch:0.23 loss:0.009 grdn:0.179 lr:5.0e-05 updt_s:0.089 data_s:0.011
+Training:   4%|█▌                                      | 979/25000 [01:48<38:17, 10.45step/s]INFO 2026-05-20 22:23:32 ot_train.py:488 step:980 smpl:63K ep:155 epch:0.23 loss:0.009 grdn:0.173 lr:5.0e-05 updt_s:0.086 data_s:0.011
+Training:   4%|█▌                                      | 999/25000 [01:50<38:42, 10.33step/s]INFO 2026-05-20 22:23:34 ot_train.py:488 step:1K smpl:64K ep:159 epch:0.23 loss:0.010 grdn:0.198 lr:5.0e-05 updt_s:0.086 data_s:0.011
+Training:   4%|█▌                                     | 1019/25000 [01:52<40:31,  9.86step/s]INFO 2026-05-20 22:23:36 ot_train.py:488 step:1K smpl:65K ep:162 epch:0.24 loss:0.009 grdn:0.173 lr:5.0e-05 updt_s:0.092 data_s:0.008
+Training:   4%|█▌                                     | 1040/25000 [01:54<42:49,  9.32step/s]INFO 2026-05-20 22:23:38 ot_train.py:488 step:1K smpl:67K ep:165 epch:0.24 loss:0.009 grdn:0.183 lr:5.0e-05 updt_s:0.087 data_s:0.013
+Training:   4%|█▋                                     | 1060/25000 [01:56<38:58, 10.24step/s]INFO 2026-05-20 22:23:40 ot_train.py:488 step:1K smpl:68K ep:168 epch:0.25 loss:0.009 grdn:0.181 lr:5.0e-05 updt_s:0.086 data_s:0.008
+Training:   4%|█▋                                     | 1080/25000 [01:58<41:35,  9.59step/s]INFO 2026-05-20 22:23:42 ot_train.py:488 step:1K smpl:69K ep:171 epch:0.25 loss:0.010 grdn:0.192 lr:5.0e-05 updt_s:0.086 data_s:0.013
+Training:   4%|█▋                                     | 1100/25000 [02:00<38:19, 10.39step/s]INFO 2026-05-20 22:23:44 ot_train.py:488 step:1K smpl:70K ep:175 epch:0.26 loss:0.009 grdn:0.180 lr:5.0e-05 updt_s:0.081 data_s:0.015
+Training:   4%|█▋                                     | 1120/25000 [02:02<41:52,  9.50step/s]INFO 2026-05-20 22:23:46 ot_train.py:488 step:1K smpl:72K ep:178 epch:0.26 loss:0.009 grdn:0.170 lr:5.0e-05 updt_s:0.091 data_s:0.008
+Training:   5%|█▊                                     | 1140/25000 [02:04<39:26, 10.08step/s]INFO 2026-05-20 22:23:48 ot_train.py:488 step:1K smpl:73K ep:181 epch:0.27 loss:0.008 grdn:0.176 lr:5.0e-05 updt_s:0.089 data_s:0.008
+Training:   5%|█▊                                     | 1160/25000 [02:06<36:24, 10.91step/s]INFO 2026-05-20 22:23:50 ot_train.py:488 step:1K smpl:74K ep:184 epch:0.27 loss:0.009 grdn:0.195 lr:5.0e-05 updt_s:0.088 data_s:0.007
+Training:   5%|█▊                                     | 1180/25000 [02:08<39:30, 10.05step/s]INFO 2026-05-20 22:23:52 ot_train.py:488 step:1K smpl:76K ep:187 epch:0.28 loss:0.009 grdn:0.168 lr:5.0e-05 updt_s:0.089 data_s:0.009
+Training:   5%|█▊                                     | 1200/25000 [02:10<40:58,  9.68step/s]INFO 2026-05-20 22:23:54 ot_train.py:488 step:1K smpl:77K ep:190 epch:0.28 loss:0.009 grdn:0.180 lr:5.0e-05 updt_s:0.087 data_s:0.008
+Training:   5%|█▉                                     | 1219/25000 [02:12<37:43, 10.50step/s]INFO 2026-05-20 22:23:56 ot_train.py:488 step:1K smpl:78K ep:194 epch:0.29 loss:0.009 grdn:0.184 lr:5.0e-05 updt_s:0.087 data_s:0.008
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+Training:   5%|█▉                                     | 1259/25000 [02:16<37:48, 10.47step/s]INFO 2026-05-20 22:24:00 ot_train.py:488 step:1K smpl:81K ep:200 epch:0.30 loss:0.009 grdn:0.180 lr:5.0e-05 updt_s:0.088 data_s:0.006
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+Training:   5%|██                                     | 1300/25000 [02:20<39:24, 10.02step/s]INFO 2026-05-20 22:24:04 ot_train.py:488 step:1K smpl:83K ep:206 epch:0.31 loss:0.009 grdn:0.172 lr:5.0e-05 updt_s:0.086 data_s:0.010
+Training:   5%|██                                     | 1320/25000 [02:22<37:50, 10.43step/s]INFO 2026-05-20 22:24:06 ot_train.py:488 step:1K smpl:84K ep:209 epch:0.31 loss:0.009 grdn:0.196 lr:5.0e-05 updt_s:0.087 data_s:0.008
+Training:   5%|██                                     | 1340/25000 [02:24<39:37,  9.95step/s]INFO 2026-05-20 22:24:08 ot_train.py:488 step:1K smpl:86K ep:213 epch:0.31 loss:0.008 grdn:0.190 lr:5.0e-05 updt_s:0.090 data_s:0.008
+Training:   5%|██                                     | 1359/25000 [02:25<38:09, 10.32step/s]INFO 2026-05-20 22:24:10 ot_train.py:488 step:1K smpl:87K ep:216 epch:0.32 loss:0.009 grdn:0.176 lr:5.0e-05 updt_s:0.089 data_s:0.008
+Training:   6%|██▏                                    | 1379/25000 [02:27<35:14, 11.17step/s]INFO 2026-05-20 22:24:12 ot_train.py:488 step:1K smpl:88K ep:219 epch:0.32 loss:0.010 grdn:0.202 lr:5.0e-05 updt_s:0.089 data_s:0.008
+Training:   6%|██▏                                    | 1399/25000 [02:29<41:22,  9.51step/s]INFO 2026-05-20 22:24:14 ot_train.py:488 step:1K smpl:90K ep:222 epch:0.33 loss:0.008 grdn:0.161 lr:5.0e-05 updt_s:0.088 data_s:0.010
+Training:   6%|██▏                                    | 1419/25000 [02:31<36:20, 10.82step/s]INFO 2026-05-20 22:24:16 ot_train.py:488 step:1K smpl:91K ep:225 epch:0.33 loss:0.010 grdn:0.198 lr:5.0e-05 updt_s:0.089 data_s:0.009
+Training:   6%|██▏                                    | 1440/25000 [02:34<40:32,  9.68step/s]INFO 2026-05-20 22:24:18 ot_train.py:488 step:1K smpl:92K ep:228 epch:0.34 loss:0.008 grdn:0.158 lr:5.0e-05 updt_s:0.094 data_s:0.006
+Training:   6%|██▎                                    | 1459/25000 [02:35<37:20, 10.51step/s]INFO 2026-05-20 22:24:20 ot_train.py:488 step:1K smpl:93K ep:232 epch:0.34 loss:0.009 grdn:0.171 lr:5.0e-05 updt_s:0.086 data_s:0.008
+Training:   6%|██▎                                    | 1480/25000 [02:37<36:12, 10.83step/s]INFO 2026-05-20 22:24:22 ot_train.py:488 step:1K smpl:95K ep:235 epch:0.35 loss:0.008 grdn:0.162 lr:5.0e-05 updt_s:0.094 data_s:0.006
+Training:   6%|██▎                                    | 1500/25000 [02:39<38:46, 10.10step/s]INFO 2026-05-20 22:24:24 ot_train.py:488 step:2K smpl:96K ep:238 epch:0.35 loss:0.009 grdn:0.186 lr:5.0e-05 updt_s:0.088 data_s:0.008
+Training:   6%|██▎                                    | 1519/25000 [02:41<37:48, 10.35step/s]INFO 2026-05-20 22:24:26 ot_train.py:488 step:2K smpl:97K ep:241 epch:0.36 loss:0.008 grdn:0.175 lr:5.0e-05 updt_s:0.094 data_s:0.008
+Training:   6%|██▍                                    | 1540/25000 [02:43<36:41, 10.66step/s]INFO 2026-05-20 22:24:28 ot_train.py:488 step:2K smpl:99K ep:244 epch:0.36 loss:0.008 grdn:0.153 lr:5.0e-05 updt_s:0.086 data_s:0.010
+Training:   6%|██▍                                    | 1560/25000 [02:45<38:40, 10.10step/s]INFO 2026-05-20 22:24:30 ot_train.py:488 step:2K smpl:100K ep:247 epch:0.37 loss:0.008 grdn:0.162 lr:5.0e-05 updt_s:0.089 data_s:0.010
+Training:   6%|██▍                                    | 1580/25000 [02:48<41:02,  9.51step/s]INFO 2026-05-20 22:24:32 ot_train.py:488 step:2K smpl:101K ep:251 epch:0.37 loss:0.008 grdn:0.174 lr:5.0e-05 updt_s:0.094 data_s:0.008
+Training:   6%|██▍                                    | 1599/25000 [02:49<39:38,  9.84step/s]INFO 2026-05-20 22:24:34 ot_train.py:488 step:2K smpl:102K ep:254 epch:0.38 loss:0.009 grdn:0.185 lr:5.0e-05 updt_s:0.088 data_s:0.006
+Training:   6%|██▌                                    | 1619/25000 [02:51<38:40, 10.08step/s]INFO 2026-05-20 22:24:36 ot_train.py:488 step:2K smpl:104K ep:257 epch:0.38 loss:0.009 grdn:0.193 lr:5.0e-05 updt_s:0.089 data_s:0.009
+Training:   7%|██▌                                    | 1639/25000 [02:53<37:02, 10.51step/s]INFO 2026-05-20 22:24:38 ot_train.py:488 step:2K smpl:105K ep:260 epch:0.38 loss:0.009 grdn:0.179 lr:5.0e-05 updt_s:0.087 data_s:0.006
+Training:   7%|██▌                                    | 1659/25000 [02:55<36:06, 10.77step/s]INFO 2026-05-20 22:24:40 ot_train.py:488 step:2K smpl:106K ep:263 epch:0.39 loss:0.008 grdn:0.168 lr:5.0e-05 updt_s:0.085 data_s:0.010
+Training:   7%|██▌                                    | 1679/25000 [02:57<39:06,  9.94step/s]INFO 2026-05-20 22:24:42 ot_train.py:488 step:2K smpl:108K ep:267 epch:0.39 loss:0.009 grdn:0.164 lr:5.0e-05 updt_s:0.090 data_s:0.008
+Training:   7%|██▋                                    | 1700/25000 [02:59<36:43, 10.58step/s]INFO 2026-05-20 22:24:44 ot_train.py:488 step:2K smpl:109K ep:270 epch:0.40 loss:0.008 grdn:0.184 lr:4.9e-05 updt_s:0.088 data_s:0.010
+Training:   7%|██▋                                    | 1719/25000 [03:01<42:37,  9.10step/s]INFO 2026-05-20 22:24:46 ot_train.py:488 step:2K smpl:110K ep:273 epch:0.40 loss:0.009 grdn:0.180 lr:4.9e-05 updt_s:0.093 data_s:0.006
+Training:   7%|██▋                                    | 1740/25000 [03:03<36:23, 10.65step/s]INFO 2026-05-20 22:24:47 ot_train.py:488 step:2K smpl:111K ep:276 epch:0.41 loss:0.008 grdn:0.185 lr:4.9e-05 updt_s:0.087 data_s:0.008
+Training:   7%|██▋                                    | 1760/25000 [03:05<39:00,  9.93step/s]INFO 2026-05-20 22:24:49 ot_train.py:488 step:2K smpl:113K ep:279 epch:0.41 loss:0.010 grdn:0.214 lr:4.9e-05 updt_s:0.089 data_s:0.009
+Training:   7%|██▊                                    | 1780/25000 [03:07<38:57,  9.93step/s]INFO 2026-05-20 22:24:51 ot_train.py:488 step:2K smpl:114K ep:282 epch:0.42 loss:0.009 grdn:0.194 lr:4.9e-05 updt_s:0.089 data_s:0.009
+Training:   7%|██▊                                    | 1799/25000 [03:09<37:51, 10.22step/s]INFO 2026-05-20 22:24:53 ot_train.py:488 step:2K smpl:115K ep:286 epch:0.42 loss:0.009 grdn:0.194 lr:4.9e-05 updt_s:0.089 data_s:0.009
+Training:   7%|██▊                                    | 1819/25000 [03:11<38:22, 10.07step/s]INFO 2026-05-20 22:24:55 ot_train.py:488 step:2K smpl:116K ep:289 epch:0.43 loss:0.008 grdn:0.169 lr:4.9e-05 updt_s:0.091 data_s:0.006
+Training:   7%|██▊                                    | 1840/25000 [03:13<39:59,  9.65step/s]INFO 2026-05-20 22:24:57 ot_train.py:488 step:2K smpl:118K ep:292 epch:0.43 loss:0.009 grdn:0.163 lr:4.9e-05 updt_s:0.088 data_s:0.013
+Training:   7%|██▉                                    | 1860/25000 [03:15<36:00, 10.71step/s]INFO 2026-05-20 22:24:59 ot_train.py:488 step:2K smpl:119K ep:295 epch:0.44 loss:0.009 grdn:0.178 lr:4.9e-05 updt_s:0.086 data_s:0.006
+Training:   8%|██▉                                    | 1880/25000 [03:17<40:16,  9.57step/s]INFO 2026-05-20 22:25:01 ot_train.py:488 step:2K smpl:120K ep:298 epch:0.44 loss:0.008 grdn:0.177 lr:4.9e-05 updt_s:0.093 data_s:0.009
+Training:   8%|██▉                                    | 1900/25000 [03:19<36:48, 10.46step/s]INFO 2026-05-20 22:25:03 ot_train.py:488 step:2K smpl:122K ep:301 epch:0.45 loss:0.008 grdn:0.158 lr:4.9e-05 updt_s:0.083 data_s:0.011
+Training:   8%|██▉                                    | 1920/25000 [03:21<35:28, 10.84step/s]INFO 2026-05-20 22:25:05 ot_train.py:488 step:2K smpl:123K ep:305 epch:0.45 loss:0.008 grdn:0.177 lr:4.9e-05 updt_s:0.089 data_s:0.008
+Training:   8%|███                                    | 1940/25000 [03:23<37:38, 10.21step/s]INFO 2026-05-20 22:25:07 ot_train.py:488 step:2K smpl:124K ep:308 epch:0.46 loss:0.009 grdn:0.190 lr:4.9e-05 updt_s:0.092 data_s:0.009
+Training:   8%|███                                    | 1960/25000 [03:25<38:13, 10.05step/s]INFO 2026-05-20 22:25:09 ot_train.py:488 step:2K smpl:125K ep:311 epch:0.46 loss:0.009 grdn:0.182 lr:4.9e-05 updt_s:0.088 data_s:0.010
+Training:   8%|███                                    | 1979/25000 [03:27<36:33, 10.50step/s]INFO 2026-05-20 22:25:11 ot_train.py:488 step:2K smpl:127K ep:314 epch:0.46 loss:0.008 grdn:0.161 lr:4.9e-05 updt_s:0.087 data_s:0.010
+Training:   8%|███                                    | 1999/25000 [03:29<40:34,  9.45step/s]INFO 2026-05-20 22:25:13 ot_train.py:488 step:2K smpl:128K ep:317 epch:0.47 loss:0.009 grdn:0.195 lr:4.9e-05 updt_s:0.090 data_s:0.008
+INFO 2026-05-20 22:25:13 ot_train.py:502 Checkpoint policy after step 2000
+Training:   8%|███▏                                   | 2019/25000 [03:39<52:39,  7.27step/s]INFO 2026-05-20 22:25:23 ot_train.py:488 step:2K smpl:129K ep:320 epch:0.47 loss:0.008 grdn:0.190 lr:4.9e-05 updt_s:0.072 data_s:0.006
+Training:   8%|███▏                                   | 2040/25000 [03:41<39:30,  9.68step/s]INFO 2026-05-20 22:25:26 ot_train.py:488 step:2K smpl:131K ep:324 epch:0.48 loss:0.008 grdn:0.169 lr:4.9e-05 updt_s:0.095 data_s:0.006
+Training:   8%|███▏                                   | 2060/25000 [03:43<36:38, 10.43step/s]INFO 2026-05-20 22:25:27 ot_train.py:488 step:2K smpl:132K ep:327 epch:0.48 loss:0.010 grdn:0.190 lr:4.9e-05 updt_s:0.088 data_s:0.006
+Training:   8%|███▏                                   | 2080/25000 [03:45<35:51, 10.65step/s]INFO 2026-05-20 22:25:29 ot_train.py:488 step:2K smpl:133K ep:330 epch:0.49 loss:0.009 grdn:0.176 lr:4.9e-05 updt_s:0.086 data_s:0.009
+Training:   8%|███▎                                   | 2100/25000 [03:47<36:55, 10.34step/s]INFO 2026-05-20 22:25:31 ot_train.py:488 step:2K smpl:134K ep:333 epch:0.49 loss:0.009 grdn:0.178 lr:4.9e-05 updt_s:0.085 data_s:0.008
+Training:   8%|███▎                                   | 2120/25000 [03:49<35:36, 10.71step/s]INFO 2026-05-20 22:25:33 ot_train.py:488 step:2K smpl:136K ep:336 epch:0.50 loss:0.009 grdn:0.174 lr:4.9e-05 updt_s:0.087 data_s:0.011
+Training:   9%|███▎                                   | 2140/25000 [03:51<37:09, 10.25step/s]INFO 2026-05-20 22:25:35 ot_train.py:488 step:2K smpl:137K ep:340 epch:0.50 loss:0.009 grdn:0.162 lr:4.9e-05 updt_s:0.082 data_s:0.011
+Training:   9%|███▎                                   | 2160/25000 [03:53<40:57,  9.29step/s]INFO 2026-05-20 22:25:37 ot_train.py:488 step:2K smpl:138K ep:343 epch:0.51 loss:0.008 grdn:0.159 lr:4.9e-05 updt_s:0.089 data_s:0.009
+Training:   9%|███▍                                   | 2180/25000 [03:55<36:53, 10.31step/s]INFO 2026-05-20 22:25:39 ot_train.py:488 step:2K smpl:140K ep:346 epch:0.51 loss:0.008 grdn:0.175 lr:4.9e-05 updt_s:0.085 data_s:0.008
+Training:   9%|███▍                                   | 2199/25000 [03:57<35:59, 10.56step/s]INFO 2026-05-20 22:25:41 ot_train.py:488 step:2K smpl:141K ep:349 epch:0.52 loss:0.010 grdn:0.191 lr:4.9e-05 updt_s:0.086 data_s:0.010
+Training:   9%|███▍                                   | 2219/25000 [03:58<36:23, 10.43step/s]INFO 2026-05-20 22:25:43 ot_train.py:488 step:2K smpl:142K ep:352 epch:0.52 loss:0.009 grdn:0.193 lr:4.9e-05 updt_s:0.090 data_s:0.006
+Training:   9%|███▍                                   | 2239/25000 [04:00<37:27, 10.13step/s]INFO 2026-05-20 22:25:45 ot_train.py:488 step:2K smpl:143K ep:355 epch:0.53 loss:0.009 grdn:0.189 lr:4.9e-05 updt_s:0.087 data_s:0.008
+Training:   9%|███▌                                   | 2259/25000 [04:02<36:58, 10.25step/s]INFO 2026-05-20 22:25:47 ot_train.py:488 step:2K smpl:145K ep:359 epch:0.53 loss:0.009 grdn:0.187 lr:4.9e-05 updt_s:0.088 data_s:0.010
+Training:   9%|███▌                                   | 2280/25000 [04:05<36:53, 10.27step/s]INFO 2026-05-20 22:25:49 ot_train.py:488 step:2K smpl:146K ep:362 epch:0.54 loss:0.009 grdn:0.174 lr:4.9e-05 updt_s:0.086 data_s:0.012
+Training:   9%|███▌                                   | 2300/25000 [04:06<39:25,  9.60step/s]INFO 2026-05-20 22:25:51 ot_train.py:488 step:2K smpl:147K ep:365 epch:0.54 loss:0.009 grdn:0.175 lr:4.9e-05 updt_s:0.088 data_s:0.010
+Training:   9%|███▌                                   | 2319/25000 [04:08<34:26, 10.98step/s]INFO 2026-05-20 22:25:53 ot_train.py:488 step:2K smpl:148K ep:368 epch:0.54 loss:0.009 grdn:0.160 lr:4.9e-05 updt_s:0.088 data_s:0.006
+Training:   9%|███▋                                   | 2340/25000 [04:10<35:38, 10.60step/s]INFO 2026-05-20 22:25:55 ot_train.py:488 step:2K smpl:150K ep:371 epch:0.55 loss:0.009 grdn:0.167 lr:4.9e-05 updt_s:0.091 data_s:0.006
+Training:   9%|███▋                                   | 2360/25000 [04:12<36:50, 10.24step/s]INFO 2026-05-20 22:25:57 ot_train.py:488 step:2K smpl:151K ep:374 epch:0.55 loss:0.009 grdn:0.170 lr:4.9e-05 updt_s:0.088 data_s:0.006
+Training:  10%|███▋                                   | 2379/25000 [04:14<36:52, 10.23step/s]INFO 2026-05-20 22:25:59 ot_train.py:488 step:2K smpl:152K ep:378 epch:0.56 loss:0.008 grdn:0.166 lr:4.9e-05 updt_s:0.090 data_s:0.006
+Training:  10%|███▋                                   | 2400/25000 [04:16<38:04,  9.89step/s]INFO 2026-05-20 22:26:01 ot_train.py:488 step:2K smpl:154K ep:381 epch:0.56 loss:0.008 grdn:0.174 lr:4.9e-05 updt_s:0.091 data_s:0.006
+Training:  10%|███▊                                   | 2420/25000 [04:18<37:51,  9.94step/s]INFO 2026-05-20 22:26:03 ot_train.py:488 step:2K smpl:155K ep:384 epch:0.57 loss:0.009 grdn:0.190 lr:4.9e-05 updt_s:0.087 data_s:0.009
+Training:  10%|███▊                                   | 2439/25000 [04:20<36:49, 10.21step/s]INFO 2026-05-20 22:26:04 ot_train.py:488 step:2K smpl:156K ep:387 epch:0.57 loss:0.010 grdn:0.191 lr:4.9e-05 updt_s:0.090 data_s:0.007
+Training:  10%|███▊                                   | 2459/25000 [04:22<36:22, 10.33step/s]INFO 2026-05-20 22:26:06 ot_train.py:488 step:2K smpl:157K ep:390 epch:0.58 loss:0.009 grdn:0.164 lr:4.9e-05 updt_s:0.088 data_s:0.009
+Training:  10%|███▊                                   | 2479/25000 [04:24<34:20, 10.93step/s]INFO 2026-05-20 22:26:08 ot_train.py:488 step:2K smpl:159K ep:393 epch:0.58 loss:0.009 grdn:0.167 lr:4.9e-05 updt_s:0.088 data_s:0.008
+Training:  10%|███▉                                   | 2499/25000 [04:26<36:31, 10.27step/s]INFO 2026-05-20 22:26:10 ot_train.py:488 step:2K smpl:160K ep:397 epch:0.59 loss:0.010 grdn:0.176 lr:4.9e-05 updt_s:0.090 data_s:0.007
+Training:  10%|███▉                                   | 2519/25000 [04:28<35:30, 10.55step/s]INFO 2026-05-20 22:26:12 ot_train.py:488 step:3K smpl:161K ep:400 epch:0.59 loss:0.009 grdn:0.181 lr:4.9e-05 updt_s:0.086 data_s:0.008
+Training:  10%|███▉                                   | 2539/25000 [04:30<37:00, 10.11step/s]INFO 2026-05-20 22:26:14 ot_train.py:488 step:3K smpl:163K ep:403 epch:0.60 loss:0.009 grdn:0.179 lr:4.9e-05 updt_s:0.090 data_s:0.008
+Training:  10%|███▉                                   | 2559/25000 [04:32<35:31, 10.53step/s]INFO 2026-05-20 22:26:16 ot_train.py:488 step:3K smpl:164K ep:406 epch:0.60 loss:0.008 grdn:0.161 lr:4.9e-05 updt_s:0.088 data_s:0.008
+Training:  10%|████                                   | 2579/25000 [04:34<36:02, 10.37step/s]INFO 2026-05-20 22:26:18 ot_train.py:488 step:3K smpl:165K ep:409 epch:0.61 loss:0.009 grdn:0.175 lr:4.9e-05 updt_s:0.088 data_s:0.007
+Training:  10%|████                                   | 2599/25000 [04:36<37:03, 10.08step/s]INFO 2026-05-20 22:26:20 ot_train.py:488 step:3K smpl:166K ep:412 epch:0.61 loss:0.009 grdn:0.184 lr:4.9e-05 updt_s:0.091 data_s:0.008
+Training:  10%|████                                   | 2619/25000 [04:38<36:42, 10.16step/s]INFO 2026-05-20 22:26:22 ot_train.py:488 step:3K smpl:168K ep:416 epch:0.61 loss:0.008 grdn:0.163 lr:4.9e-05 updt_s:0.088 data_s:0.008
+Training:  11%|████                                   | 2639/25000 [04:40<36:58, 10.08step/s]INFO 2026-05-20 22:26:24 ot_train.py:488 step:3K smpl:169K ep:419 epch:0.62 loss:0.009 grdn:0.184 lr:4.9e-05 updt_s:0.089 data_s:0.010
+Training:  11%|████▏                                  | 2659/25000 [04:42<35:50, 10.39step/s]INFO 2026-05-20 22:26:26 ot_train.py:488 step:3K smpl:170K ep:422 epch:0.62 loss:0.008 grdn:0.173 lr:4.9e-05 updt_s:0.087 data_s:0.009
+Training:  11%|████▏                                  | 2679/25000 [04:44<35:56, 10.35step/s]INFO 2026-05-20 22:26:28 ot_train.py:488 step:3K smpl:172K ep:425 epch:0.63 loss:0.009 grdn:0.181 lr:4.9e-05 updt_s:0.085 data_s:0.013
+Training:  11%|████▏                                  | 2699/25000 [04:46<36:18, 10.24step/s]INFO 2026-05-20 22:26:30 ot_train.py:488 step:3K smpl:173K ep:428 epch:0.63 loss:0.009 grdn:0.169 lr:4.9e-05 updt_s:0.088 data_s:0.008
+Training:  11%|████▏                                  | 2719/25000 [04:48<37:10,  9.99step/s]INFO 2026-05-20 22:26:32 ot_train.py:488 step:3K smpl:174K ep:432 epch:0.64 loss:0.009 grdn:0.166 lr:4.9e-05 updt_s:0.089 data_s:0.008
+Training:  11%|████▎                                  | 2740/25000 [04:50<36:37, 10.13step/s]INFO 2026-05-20 22:26:34 ot_train.py:488 step:3K smpl:175K ep:435 epch:0.64 loss:0.008 grdn:0.169 lr:4.9e-05 updt_s:0.087 data_s:0.011
+Training:  11%|████▎                                  | 2760/25000 [04:52<35:38, 10.40step/s]INFO 2026-05-20 22:26:36 ot_train.py:488 step:3K smpl:177K ep:438 epch:0.65 loss:0.009 grdn:0.178 lr:4.9e-05 updt_s:0.088 data_s:0.007
+Training:  11%|████▎                                  | 2780/25000 [04:54<36:44, 10.08step/s]INFO 2026-05-20 22:26:38 ot_train.py:488 step:3K smpl:178K ep:441 epch:0.65 loss:0.008 grdn:0.175 lr:4.9e-05 updt_s:0.088 data_s:0.009
+Training:  11%|████▎                                  | 2800/25000 [04:56<39:49,  9.29step/s]INFO 2026-05-20 22:26:40 ot_train.py:488 step:3K smpl:179K ep:444 epch:0.66 loss:0.008 grdn:0.176 lr:4.9e-05 updt_s:0.091 data_s:0.010
+Training:  11%|████▍                                  | 2820/25000 [04:58<34:36, 10.68step/s]INFO 2026-05-20 22:26:42 ot_train.py:488 step:3K smpl:180K ep:447 epch:0.66 loss:0.008 grdn:0.167 lr:4.9e-05 updt_s:0.084 data_s:0.010
+Training:  11%|████▍                                  | 2840/25000 [05:00<36:18, 10.17step/s]INFO 2026-05-20 22:26:44 ot_train.py:488 step:3K smpl:182K ep:451 epch:0.67 loss:0.008 grdn:0.166 lr:4.9e-05 updt_s:0.091 data_s:0.008
+Training:  11%|████▍                                  | 2860/25000 [05:02<35:30, 10.39step/s]INFO 2026-05-20 22:26:46 ot_train.py:488 step:3K smpl:183K ep:454 epch:0.67 loss:0.009 grdn:0.190 lr:4.9e-05 updt_s:0.085 data_s:0.013
+Training:  12%|████▍                                  | 2880/25000 [05:04<35:46, 10.31step/s]INFO 2026-05-20 22:26:48 ot_train.py:488 step:3K smpl:184K ep:457 epch:0.68 loss:0.009 grdn:0.183 lr:4.8e-05 updt_s:0.090 data_s:0.008
+Training:  12%|████▌                                  | 2900/25000 [05:05<36:29, 10.10step/s]INFO 2026-05-20 22:26:50 ot_train.py:488 step:3K smpl:186K ep:460 epch:0.68 loss:0.010 grdn:0.178 lr:4.8e-05 updt_s:0.090 data_s:0.006
+Training:  12%|████▌                                  | 2919/25000 [05:07<38:33,  9.55step/s]INFO 2026-05-20 22:26:52 ot_train.py:488 step:3K smpl:187K ep:463 epch:0.69 loss:0.009 grdn:0.185 lr:4.8e-05 updt_s:0.089 data_s:0.010
+Training:  12%|████▌                                  | 2940/25000 [05:09<36:38, 10.03step/s]INFO 2026-05-20 22:26:54 ot_train.py:488 step:3K smpl:188K ep:466 epch:0.69 loss:0.009 grdn:0.181 lr:4.8e-05 updt_s:0.091 data_s:0.008
+Training:  12%|████▌                                  | 2960/25000 [05:11<34:23, 10.68step/s]INFO 2026-05-20 22:26:56 ot_train.py:488 step:3K smpl:189K ep:470 epch:0.69 loss:0.009 grdn:0.168 lr:4.8e-05 updt_s:0.088 data_s:0.008
+Training:  12%|████▋                                  | 2980/25000 [05:13<36:28, 10.06step/s]INFO 2026-05-20 22:26:58 ot_train.py:488 step:3K smpl:191K ep:473 epch:0.70 loss:0.010 grdn:0.188 lr:4.8e-05 updt_s:0.090 data_s:0.007
+Training:  12%|████▋                                  | 2999/25000 [05:15<35:51, 10.23step/s]INFO 2026-05-20 22:27:00 ot_train.py:488 step:3K smpl:192K ep:476 epch:0.70 loss:0.009 grdn:0.175 lr:4.8e-05 updt_s:0.089 data_s:0.011
+Training:  12%|████▋                                  | 3019/25000 [05:17<37:37,  9.74step/s]INFO 2026-05-20 22:27:02 ot_train.py:488 step:3K smpl:193K ep:479 epch:0.71 loss:0.009 grdn:0.183 lr:4.8e-05 updt_s:0.089 data_s:0.007
+Training:  12%|████▋                                  | 3039/25000 [05:19<36:17, 10.08step/s]INFO 2026-05-20 22:27:04 ot_train.py:488 step:3K smpl:195K ep:482 epch:0.71 loss:0.009 grdn:0.187 lr:4.8e-05 updt_s:0.085 data_s:0.011
+Training:  12%|████▊                                  | 3060/25000 [05:21<40:49,  8.96step/s]INFO 2026-05-20 22:27:06 ot_train.py:488 step:3K smpl:196K ep:485 epch:0.72 loss:0.009 grdn:0.189 lr:4.8e-05 updt_s:0.097 data_s:0.007
+Training:  12%|████▊                                  | 3080/25000 [05:23<36:30, 10.01step/s]INFO 2026-05-20 22:27:08 ot_train.py:488 step:3K smpl:197K ep:489 epch:0.72 loss:0.008 grdn:0.169 lr:4.8e-05 updt_s:0.086 data_s:0.011
+Training:  12%|████▊                                  | 3100/25000 [05:25<34:44, 10.51step/s]INFO 2026-05-20 22:27:10 ot_train.py:488 step:3K smpl:198K ep:492 epch:0.73 loss:0.010 grdn:0.187 lr:4.8e-05 updt_s:0.086 data_s:0.014
+Training:  12%|████▊                                  | 3119/25000 [05:27<35:28, 10.28step/s]INFO 2026-05-20 22:27:12 ot_train.py:488 step:3K smpl:200K ep:495 epch:0.73 loss:0.009 grdn:0.187 lr:4.8e-05 updt_s:0.086 data_s:0.011
+Training:  13%|████▉                                  | 3140/25000 [05:29<36:47,  9.90step/s]INFO 2026-05-20 22:27:14 ot_train.py:488 step:3K smpl:201K ep:498 epch:0.74 loss:0.008 grdn:0.160 lr:4.8e-05 updt_s:0.090 data_s:0.010
+Training:  13%|████▉                                  | 3160/25000 [05:31<36:14, 10.04step/s]INFO 2026-05-20 22:27:16 ot_train.py:488 step:3K smpl:202K ep:501 epch:0.74 loss:0.009 grdn:0.171 lr:4.8e-05 updt_s:0.091 data_s:0.006
+Training:  13%|████▉                                  | 3180/25000 [05:33<35:19, 10.30step/s]INFO 2026-05-20 22:27:18 ot_train.py:488 step:3K smpl:204K ep:504 epch:0.75 loss:0.008 grdn:0.165 lr:4.8e-05 updt_s:0.088 data_s:0.008
+Training:  13%|████▉                                  | 3200/25000 [05:35<35:41, 10.18step/s]INFO 2026-05-20 22:27:20 ot_train.py:488 step:3K smpl:205K ep:508 epch:0.75 loss:0.009 grdn:0.165 lr:4.8e-05 updt_s:0.089 data_s:0.009
+Training:  13%|█████                                  | 3219/25000 [05:37<36:03, 10.07step/s]INFO 2026-05-20 22:27:22 ot_train.py:488 step:3K smpl:206K ep:511 epch:0.76 loss:0.009 grdn:0.169 lr:4.8e-05 updt_s:0.089 data_s:0.009
+Training:  13%|█████                                  | 3240/25000 [05:39<36:48,  9.86step/s]INFO 2026-05-20 22:27:24 ot_train.py:488 step:3K smpl:207K ep:514 epch:0.76 loss:0.009 grdn:0.170 lr:4.8e-05 updt_s:0.092 data_s:0.008
+Training:  13%|█████                                  | 3259/25000 [05:41<34:07, 10.62step/s]INFO 2026-05-20 22:27:26 ot_train.py:488 step:3K smpl:209K ep:517 epch:0.77 loss:0.008 grdn:0.165 lr:4.8e-05 updt_s:0.085 data_s:0.011
+Training:  13%|█████                                  | 3279/25000 [05:43<34:53, 10.38step/s]INFO 2026-05-20 22:27:28 ot_train.py:488 step:3K smpl:210K ep:520 epch:0.77 loss:0.009 grdn:0.165 lr:4.8e-05 updt_s:0.093 data_s:0.010
+Training:  13%|█████▏                                 | 3300/25000 [05:45<36:09, 10.00step/s]INFO 2026-05-20 22:27:30 ot_train.py:488 step:3K smpl:211K ep:524 epch:0.77 loss:0.008 grdn:0.170 lr:4.8e-05 updt_s:0.089 data_s:0.009
+Training:  13%|█████▏                                 | 3320/25000 [05:47<37:28,  9.64step/s]INFO 2026-05-20 22:27:32 ot_train.py:488 step:3K smpl:212K ep:527 epch:0.78 loss:0.008 grdn:0.164 lr:4.8e-05 updt_s:0.083 data_s:0.012
+Training:  13%|█████▏                                 | 3340/25000 [05:49<34:49, 10.37step/s]INFO 2026-05-20 22:27:33 ot_train.py:488 step:3K smpl:214K ep:530 epch:0.78 loss:0.009 grdn:0.179 lr:4.8e-05 updt_s:0.086 data_s:0.011
+Training:  13%|█████▏                                 | 3360/25000 [05:51<37:05,  9.72step/s]INFO 2026-05-20 22:27:36 ot_train.py:488 step:3K smpl:215K ep:533 epch:0.79 loss:0.008 grdn:0.162 lr:4.8e-05 updt_s:0.091 data_s:0.009
+Training:  14%|█████▎                                 | 3380/25000 [05:53<32:14, 11.18step/s]INFO 2026-05-20 22:27:37 ot_train.py:488 step:3K smpl:216K ep:536 epch:0.79 loss:0.008 grdn:0.168 lr:4.8e-05 updt_s:0.085 data_s:0.006
+Training:  14%|█████▎                                 | 3399/25000 [05:55<33:42, 10.68step/s]INFO 2026-05-20 22:27:39 ot_train.py:488 step:3K smpl:218K ep:539 epch:0.80 loss:0.009 grdn:0.164 lr:4.8e-05 updt_s:0.093 data_s:0.008
+Training:  14%|█████▎                                 | 3420/25000 [05:57<37:16,  9.65step/s]INFO 2026-05-20 22:27:41 ot_train.py:488 step:3K smpl:219K ep:543 epch:0.80 loss:0.008 grdn:0.163 lr:4.8e-05 updt_s:0.088 data_s:0.008
+Training:  14%|█████▎                                 | 3440/25000 [05:59<36:26,  9.86step/s]INFO 2026-05-20 22:27:43 ot_train.py:488 step:3K smpl:220K ep:546 epch:0.81 loss:0.008 grdn:0.163 lr:4.8e-05 updt_s:0.090 data_s:0.007
+Training:  14%|█████▍                                 | 3460/25000 [06:01<33:26, 10.73step/s]INFO 2026-05-20 22:27:45 ot_train.py:488 step:3K smpl:221K ep:549 epch:0.81 loss:0.009 grdn:0.185 lr:4.8e-05 updt_s:0.087 data_s:0.010
+Training:  14%|█████▍                                 | 3479/25000 [06:03<30:31, 11.75step/s]INFO 2026-05-20 22:27:47 ot_train.py:488 step:3K smpl:223K ep:552 epch:0.82 loss:0.009 grdn:0.188 lr:4.8e-05 updt_s:0.086 data_s:0.006
+Training:  14%|█████▍                                 | 3499/25000 [06:05<34:42, 10.33step/s]INFO 2026-05-20 22:27:49 ot_train.py:488 step:4K smpl:224K ep:555 epch:0.82 loss:0.009 grdn:0.190 lr:4.8e-05 updt_s:0.086 data_s:0.013
+Training:  14%|█████▍                                 | 3519/25000 [06:07<35:27, 10.10step/s]INFO 2026-05-20 22:27:51 ot_train.py:488 step:4K smpl:225K ep:558 epch:0.83 loss:0.010 grdn:0.183 lr:4.8e-05 updt_s:0.093 data_s:0.008
+Training:  14%|█████▌                                 | 3539/25000 [06:09<34:40, 10.32step/s]INFO 2026-05-20 22:27:53 ot_train.py:488 step:4K smpl:227K ep:562 epch:0.83 loss:0.009 grdn:0.177 lr:4.8e-05 updt_s:0.090 data_s:0.008
+Training:  14%|█████▌                                 | 3559/25000 [06:11<34:13, 10.44step/s]INFO 2026-05-20 22:27:55 ot_train.py:488 step:4K smpl:228K ep:565 epch:0.84 loss:0.009 grdn:0.176 lr:4.8e-05 updt_s:0.091 data_s:0.008
+Training:  14%|█████▌                                 | 3580/25000 [06:13<33:05, 10.79step/s]INFO 2026-05-20 22:27:57 ot_train.py:488 step:4K smpl:229K ep:568 epch:0.84 loss:0.009 grdn:0.170 lr:4.8e-05 updt_s:0.087 data_s:0.008
+Training:  14%|█████▌                                 | 3600/25000 [06:15<36:52,  9.67step/s]INFO 2026-05-20 22:27:59 ot_train.py:488 step:4K smpl:230K ep:571 epch:0.84 loss:0.009 grdn:0.173 lr:4.8e-05 updt_s:0.089 data_s:0.011
+Training:  14%|█████▋                                 | 3620/25000 [06:17<33:29, 10.64step/s]INFO 2026-05-20 22:28:01 ot_train.py:488 step:4K smpl:232K ep:574 epch:0.85 loss:0.010 grdn:0.180 lr:4.8e-05 updt_s:0.086 data_s:0.008
+Training:  15%|█████▋                                 | 3640/25000 [06:19<34:54, 10.20step/s]INFO 2026-05-20 22:28:03 ot_train.py:488 step:4K smpl:233K ep:577 epch:0.85 loss:0.009 grdn:0.170 lr:4.8e-05 updt_s:0.090 data_s:0.008
+Training:  15%|█████▋                                 | 3660/25000 [06:21<35:33, 10.00step/s]INFO 2026-05-20 22:28:05 ot_train.py:488 step:4K smpl:234K ep:581 epch:0.86 loss:0.009 grdn:0.170 lr:4.8e-05 updt_s:0.088 data_s:0.011
+Training:  15%|█████▋                                 | 3680/25000 [06:23<35:31, 10.00step/s]INFO 2026-05-20 22:28:07 ot_train.py:488 step:4K smpl:236K ep:584 epch:0.86 loss:0.010 grdn:0.181 lr:4.8e-05 updt_s:0.089 data_s:0.012
+Training:  15%|█████▊                                 | 3699/25000 [06:25<33:39, 10.55step/s]INFO 2026-05-20 22:28:09 ot_train.py:488 step:4K smpl:237K ep:587 epch:0.87 loss:0.008 grdn:0.175 lr:4.7e-05 updt_s:0.087 data_s:0.008
+Training:  15%|█████▊                                 | 3719/25000 [06:27<35:47,  9.91step/s]INFO 2026-05-20 22:28:11 ot_train.py:488 step:4K smpl:238K ep:590 epch:0.87 loss:0.009 grdn:0.176 lr:4.7e-05 updt_s:0.091 data_s:0.008
+Training:  15%|█████▊                                 | 3740/25000 [06:29<35:13, 10.06step/s]INFO 2026-05-20 22:28:13 ot_train.py:488 step:4K smpl:239K ep:593 epch:0.88 loss:0.009 grdn:0.175 lr:4.7e-05 updt_s:0.092 data_s:0.006
+Training:  15%|█████▊                                 | 3760/25000 [06:31<34:26, 10.28step/s]INFO 2026-05-20 22:28:15 ot_train.py:488 step:4K smpl:241K ep:597 epch:0.88 loss:0.008 grdn:0.168 lr:4.7e-05 updt_s:0.091 data_s:0.012
+Training:  15%|█████▉                                 | 3779/25000 [06:33<38:41,  9.14step/s]INFO 2026-05-20 22:28:17 ot_train.py:488 step:4K smpl:242K ep:600 epch:0.89 loss:0.009 grdn:0.172 lr:4.7e-05 updt_s:0.085 data_s:0.015
+Training:  15%|█████▉                                 | 3799/25000 [06:35<33:22, 10.59step/s]INFO 2026-05-20 22:28:19 ot_train.py:488 step:4K smpl:243K ep:603 epch:0.89 loss:0.009 grdn:0.188 lr:4.7e-05 updt_s:0.088 data_s:0.009
+Training:  15%|█████▉                                 | 3819/25000 [06:37<33:04, 10.68step/s]INFO 2026-05-20 22:28:21 ot_train.py:488 step:4K smpl:244K ep:606 epch:0.90 loss:0.008 grdn:0.158 lr:4.7e-05 updt_s:0.088 data_s:0.009
+Training:  15%|█████▉                                 | 3839/25000 [06:39<34:41, 10.16step/s]INFO 2026-05-20 22:28:23 ot_train.py:488 step:4K smpl:246K ep:609 epch:0.90 loss:0.009 grdn:0.175 lr:4.7e-05 updt_s:0.091 data_s:0.006
+Training:  15%|██████                                 | 3859/25000 [06:41<35:41,  9.87step/s]INFO 2026-05-20 22:28:25 ot_train.py:488 step:4K smpl:247K ep:612 epch:0.91 loss:0.009 grdn:0.167 lr:4.7e-05 updt_s:0.090 data_s:0.006
+Training:  16%|██████                                 | 3880/25000 [06:43<33:55, 10.37step/s]INFO 2026-05-20 22:28:27 ot_train.py:488 step:4K smpl:248K ep:616 epch:0.91 loss:0.009 grdn:0.174 lr:4.7e-05 updt_s:0.092 data_s:0.006
+Training:  16%|██████                                 | 3899/25000 [06:45<33:28, 10.51step/s]INFO 2026-05-20 22:28:29 ot_train.py:488 step:4K smpl:250K ep:619 epch:0.92 loss:0.009 grdn:0.177 lr:4.7e-05 updt_s:0.088 data_s:0.008
+Training:  16%|██████                                 | 3920/25000 [06:47<36:17,  9.68step/s]INFO 2026-05-20 22:28:31 ot_train.py:488 step:4K smpl:251K ep:622 epch:0.92 loss:0.009 grdn:0.164 lr:4.7e-05 updt_s:0.089 data_s:0.012
+Training:  16%|██████▏                                | 3940/25000 [06:49<35:37,  9.85step/s]INFO 2026-05-20 22:28:33 ot_train.py:488 step:4K smpl:252K ep:625 epch:0.92 loss:0.008 grdn:0.158 lr:4.7e-05 updt_s:0.085 data_s:0.014
+Training:  16%|██████▏                                | 3960/25000 [06:51<33:11, 10.57step/s]INFO 2026-05-20 22:28:35 ot_train.py:488 step:4K smpl:253K ep:628 epch:0.93 loss:0.010 grdn:0.172 lr:4.7e-05 updt_s:0.089 data_s:0.008
+Training:  16%|██████▏                                | 3979/25000 [06:53<35:17,  9.93step/s]INFO 2026-05-20 22:28:37 ot_train.py:488 step:4K smpl:255K ep:631 epch:0.93 loss:0.009 grdn:0.171 lr:4.7e-05 updt_s:0.092 data_s:0.009
+Training:  16%|██████▏                                | 4000/25000 [06:55<36:49,  9.50step/s]INFO 2026-05-20 22:28:39 ot_train.py:488 step:4K smpl:256K ep:635 epch:0.94 loss:0.009 grdn:0.172 lr:4.7e-05 updt_s:0.089 data_s:0.009
+INFO 2026-05-20 22:28:39 ot_train.py:502 Checkpoint policy after step 4000
+Training:  16%|██████▎                                | 4019/25000 [07:04<49:10,  7.11step/s]INFO 2026-05-20 22:28:49 ot_train.py:488 step:4K smpl:257K ep:638 epch:0.94 loss:0.009 grdn:0.170 lr:4.7e-05 updt_s:0.071 data_s:0.008
+Training:  16%|██████▎                                | 4040/25000 [07:07<34:30, 10.12step/s]INFO 2026-05-20 22:28:51 ot_train.py:488 step:4K smpl:259K ep:641 epch:0.95 loss:0.009 grdn:0.178 lr:4.7e-05 updt_s:0.091 data_s:0.012
+Training:  16%|██████▎                                | 4059/25000 [07:08<33:03, 10.56step/s]INFO 2026-05-20 22:28:53 ot_train.py:488 step:4K smpl:260K ep:644 epch:0.95 loss:0.009 grdn:0.207 lr:4.7e-05 updt_s:0.085 data_s:0.010
+Training:  16%|██████▎                                | 4079/25000 [07:10<33:43, 10.34step/s]INFO 2026-05-20 22:28:55 ot_train.py:488 step:4K smpl:261K ep:647 epch:0.96 loss:0.009 grdn:0.174 lr:4.7e-05 updt_s:0.087 data_s:0.010
+Training:  16%|██████▍                                | 4099/25000 [07:12<32:02, 10.87step/s]INFO 2026-05-20 22:28:57 ot_train.py:488 step:4K smpl:262K ep:650 epch:0.96 loss:0.009 grdn:0.186 lr:4.7e-05 updt_s:0.087 data_s:0.009
+Training:  16%|██████▍                                | 4119/25000 [07:14<33:02, 10.53step/s]INFO 2026-05-20 22:28:59 ot_train.py:488 step:4K smpl:264K ep:654 epch:0.97 loss:0.008 grdn:0.178 lr:4.7e-05 updt_s:0.081 data_s:0.014
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+Training:  18%|███████                                | 4539/25000 [08:00<32:35, 10.47step/s]INFO 2026-05-20 22:29:44 ot_train.py:488 step:5K smpl:291K ep:720 epch:1.07 loss:0.009 grdn:0.163 lr:4.6e-05 updt_s:0.087 data_s:0.009
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+Training:  19%|███████▍                               | 4780/25000 [08:24<31:15, 10.78step/s]INFO 2026-05-20 22:30:08 ot_train.py:488 step:5K smpl:306K ep:758 epch:1.12 loss:0.008 grdn:0.160 lr:4.6e-05 updt_s:0.083 data_s:0.008
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+Training:  19%|███████▌                               | 4840/25000 [08:30<32:40, 10.28step/s]INFO 2026-05-20 22:30:14 ot_train.py:488 step:5K smpl:310K ep:768 epch:1.14 loss:0.009 grdn:0.199 lr:4.6e-05 updt_s:0.088 data_s:0.008
+Training:  19%|███████▌                               | 4860/25000 [08:32<32:52, 10.21step/s]INFO 2026-05-20 22:30:16 ot_train.py:488 step:5K smpl:311K ep:771 epch:1.14 loss:0.009 grdn:0.176 lr:4.6e-05 updt_s:0.088 data_s:0.012
+Training:  20%|███████▌                               | 4880/25000 [08:34<35:00,  9.58step/s]INFO 2026-05-20 22:30:18 ot_train.py:488 step:5K smpl:312K ep:774 epch:1.15 loss:0.009 grdn:0.184 lr:4.6e-05 updt_s:0.092 data_s:0.006
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+Training:  20%|███████▋                               | 4940/25000 [08:40<36:03,  9.27step/s]INFO 2026-05-20 22:30:24 ot_train.py:488 step:5K smpl:316K ep:784 epch:1.16 loss:0.009 grdn:0.175 lr:4.5e-05 updt_s:0.093 data_s:0.008
+Training:  20%|███████▋                               | 4960/25000 [08:41<32:38, 10.23step/s]INFO 2026-05-20 22:30:26 ot_train.py:488 step:5K smpl:317K ep:787 epch:1.16 loss:0.009 grdn:0.177 lr:4.5e-05 updt_s:0.086 data_s:0.009
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+Training:  20%|███████▊                               | 5000/25000 [08:45<35:05,  9.50step/s]INFO 2026-05-20 22:30:30 ot_train.py:488 step:5K smpl:320K ep:793 epch:1.17 loss:0.009 grdn:0.177 lr:4.5e-05 updt_s:0.089 data_s:0.009
+Training:  20%|███████▊                               | 5019/25000 [08:47<32:15, 10.32step/s]INFO 2026-05-20 22:30:32 ot_train.py:488 step:5K smpl:321K ep:796 epch:1.18 loss:0.009 grdn:0.183 lr:4.5e-05 updt_s:0.088 data_s:0.009
+Training:  20%|███████▊                               | 5040/25000 [08:49<32:06, 10.36step/s]INFO 2026-05-20 22:30:34 ot_train.py:488 step:5K smpl:323K ep:800 epch:1.18 loss:0.009 grdn:0.167 lr:4.5e-05 updt_s:0.087 data_s:0.010
+Training:  20%|███████▉                               | 5060/25000 [08:51<30:55, 10.74step/s]INFO 2026-05-20 22:30:36 ot_train.py:488 step:5K smpl:324K ep:803 epch:1.19 loss:0.009 grdn:0.180 lr:4.5e-05 updt_s:0.085 data_s:0.008
+Training:  20%|███████▉                               | 5080/25000 [08:53<31:09, 10.66step/s]INFO 2026-05-20 22:30:38 ot_train.py:488 step:5K smpl:325K ep:806 epch:1.19 loss:0.010 grdn:0.179 lr:4.5e-05 updt_s:0.090 data_s:0.007
+Training:  20%|███████▉                               | 5100/25000 [08:55<30:39, 10.82step/s]INFO 2026-05-20 22:30:39 ot_train.py:488 step:5K smpl:326K ep:809 epch:1.20 loss:0.009 grdn:0.182 lr:4.5e-05 updt_s:0.087 data_s:0.009
+Training:  20%|███████▉                               | 5120/25000 [08:57<31:48, 10.42step/s]INFO 2026-05-20 22:30:42 ot_train.py:488 step:5K smpl:328K ep:812 epch:1.20 loss:0.009 grdn:0.166 lr:4.5e-05 updt_s:0.090 data_s:0.011
+Training:  21%|████████                               | 5140/25000 [08:59<33:13,  9.96step/s]INFO 2026-05-20 22:30:43 ot_train.py:488 step:5K smpl:329K ep:815 epch:1.21 loss:0.009 grdn:0.163 lr:4.5e-05 updt_s:0.087 data_s:0.008
+Training:  21%|████████                               | 5160/25000 [09:01<32:01, 10.32step/s]INFO 2026-05-20 22:30:45 ot_train.py:488 step:5K smpl:330K ep:819 epch:1.21 loss:0.008 grdn:0.167 lr:4.5e-05 updt_s:0.086 data_s:0.010
+Training:  21%|████████                               | 5180/25000 [09:03<34:28,  9.58step/s]INFO 2026-05-20 22:30:47 ot_train.py:488 step:5K smpl:332K ep:822 epch:1.22 loss:0.008 grdn:0.162 lr:4.5e-05 updt_s:0.091 data_s:0.011
+Training:  21%|████████                               | 5200/25000 [09:05<31:31, 10.47step/s]INFO 2026-05-20 22:30:49 ot_train.py:488 step:5K smpl:333K ep:825 epch:1.22 loss:0.009 grdn:0.171 lr:4.5e-05 updt_s:0.086 data_s:0.006
+Training:  21%|████████▏                              | 5220/25000 [09:07<31:17, 10.53step/s]INFO 2026-05-20 22:30:51 ot_train.py:488 step:5K smpl:334K ep:828 epch:1.23 loss:0.007 grdn:0.155 lr:4.5e-05 updt_s:0.087 data_s:0.011
+Training:  21%|████████▏                              | 5239/25000 [09:09<31:56, 10.31step/s]INFO 2026-05-20 22:30:53 ot_train.py:488 step:5K smpl:335K ep:831 epch:1.23 loss:0.008 grdn:0.178 lr:4.5e-05 updt_s:0.084 data_s:0.013
+Training:  21%|████████▏                              | 5260/25000 [09:11<33:48,  9.73step/s]INFO 2026-05-20 22:30:55 ot_train.py:488 step:5K smpl:337K ep:834 epch:1.23 loss:0.009 grdn:0.170 lr:4.5e-05 updt_s:0.087 data_s:0.012
+Training:  21%|████████▏                              | 5280/25000 [09:13<32:44, 10.04step/s]INFO 2026-05-20 22:30:57 ot_train.py:488 step:5K smpl:338K ep:838 epch:1.24 loss:0.007 grdn:0.160 lr:4.5e-05 updt_s:0.090 data_s:0.007
+Training:  21%|████████▎                              | 5300/25000 [09:15<31:04, 10.57step/s]INFO 2026-05-20 22:30:59 ot_train.py:488 step:5K smpl:339K ep:841 epch:1.24 loss:0.007 grdn:0.154 lr:4.5e-05 updt_s:0.089 data_s:0.008
+Training:  21%|████████▎                              | 5320/25000 [09:17<31:35, 10.38step/s]INFO 2026-05-20 22:31:01 ot_train.py:488 step:5K smpl:340K ep:844 epch:1.25 loss:0.009 grdn:0.165 lr:4.5e-05 updt_s:0.086 data_s:0.011
+Training:  21%|████████▎                              | 5340/25000 [09:19<32:36, 10.05step/s]INFO 2026-05-20 22:31:03 ot_train.py:488 step:5K smpl:342K ep:847 epch:1.25 loss:0.008 grdn:0.154 lr:4.5e-05 updt_s:0.090 data_s:0.009
+Training:  21%|████████▎                              | 5360/25000 [09:21<32:10, 10.17step/s]INFO 2026-05-20 22:31:05 ot_train.py:488 step:5K smpl:343K ep:850 epch:1.26 loss:0.009 grdn:0.180 lr:4.5e-05 updt_s:0.088 data_s:0.011
+Training:  22%|████████▍                              | 5379/25000 [09:23<30:22, 10.76step/s]INFO 2026-05-20 22:31:07 ot_train.py:488 step:5K smpl:344K ep:854 epch:1.26 loss:0.009 grdn:0.168 lr:4.5e-05 updt_s:0.089 data_s:0.006
+Training:  22%|████████▍                              | 5399/25000 [09:25<31:13, 10.46step/s]INFO 2026-05-20 22:31:09 ot_train.py:488 step:5K smpl:346K ep:857 epch:1.27 loss:0.008 grdn:0.164 lr:4.5e-05 updt_s:0.091 data_s:0.006
+Training:  22%|████████▍                              | 5419/25000 [09:27<31:42, 10.29step/s]INFO 2026-05-20 22:31:11 ot_train.py:488 step:5K smpl:347K ep:860 epch:1.27 loss:0.009 grdn:0.165 lr:4.5e-05 updt_s:0.089 data_s:0.008
+Training:  22%|████████▍                              | 5439/25000 [09:29<31:16, 10.42step/s]INFO 2026-05-20 22:31:13 ot_train.py:488 step:5K smpl:348K ep:863 epch:1.28 loss:0.009 grdn:0.189 lr:4.5e-05 updt_s:0.088 data_s:0.006
+Training:  22%|████████▌                              | 5459/25000 [09:31<30:22, 10.72step/s]INFO 2026-05-20 22:31:15 ot_train.py:488 step:5K smpl:349K ep:866 epch:1.28 loss:0.009 grdn:0.187 lr:4.5e-05 updt_s:0.087 data_s:0.008
+Training:  22%|████████▌                              | 5480/25000 [09:33<30:48, 10.56step/s]INFO 2026-05-20 22:31:17 ot_train.py:488 step:5K smpl:351K ep:869 epch:1.29 loss:0.009 grdn:0.192 lr:4.4e-05 updt_s:0.088 data_s:0.009
+Training:  22%|████████▌                              | 5499/25000 [09:35<32:04, 10.14step/s]INFO 2026-05-20 22:31:19 ot_train.py:488 step:6K smpl:352K ep:873 epch:1.29 loss:0.009 grdn:0.184 lr:4.4e-05 updt_s:0.087 data_s:0.011
+Training:  22%|████████▌                              | 5519/25000 [09:37<32:25, 10.01step/s]INFO 2026-05-20 22:31:21 ot_train.py:488 step:6K smpl:353K ep:876 epch:1.30 loss:0.009 grdn:0.175 lr:4.4e-05 updt_s:0.088 data_s:0.011
+Training:  22%|████████▋                              | 5539/25000 [09:39<30:59, 10.46step/s]INFO 2026-05-20 22:31:23 ot_train.py:488 step:6K smpl:355K ep:879 epch:1.30 loss:0.008 grdn:0.159 lr:4.4e-05 updt_s:0.091 data_s:0.006
+Training:  22%|████████▋                              | 5559/25000 [09:41<33:23,  9.70step/s]INFO 2026-05-20 22:31:25 ot_train.py:488 step:6K smpl:356K ep:882 epch:1.30 loss:0.009 grdn:0.170 lr:4.4e-05 updt_s:0.086 data_s:0.011
+Training:  22%|████████▋                              | 5579/25000 [09:42<32:49,  9.86step/s]INFO 2026-05-20 22:31:27 ot_train.py:488 step:6K smpl:357K ep:885 epch:1.31 loss:0.008 grdn:0.157 lr:4.4e-05 updt_s:0.089 data_s:0.008
+Training:  22%|████████▋                              | 5600/25000 [09:45<30:43, 10.52step/s]INFO 2026-05-20 22:31:29 ot_train.py:488 step:6K smpl:358K ep:888 epch:1.31 loss:0.009 grdn:0.166 lr:4.4e-05 updt_s:0.086 data_s:0.011
+Training:  22%|████████▊                              | 5619/25000 [09:46<33:15,  9.71step/s]INFO 2026-05-20 22:31:31 ot_train.py:488 step:6K smpl:360K ep:892 epch:1.32 loss:0.009 grdn:0.177 lr:4.4e-05 updt_s:0.092 data_s:0.008
+Training:  23%|████████▊                              | 5639/25000 [09:48<30:00, 10.75step/s]INFO 2026-05-20 22:31:33 ot_train.py:488 step:6K smpl:361K ep:895 epch:1.32 loss:0.008 grdn:0.178 lr:4.4e-05 updt_s:0.089 data_s:0.006
+Training:  23%|████████▊                              | 5660/25000 [09:51<34:58,  9.22step/s]INFO 2026-05-20 22:31:35 ot_train.py:488 step:6K smpl:362K ep:898 epch:1.33 loss:0.008 grdn:0.156 lr:4.4e-05 updt_s:0.093 data_s:0.008
+Training:  23%|████████▊                              | 5679/25000 [09:52<31:22, 10.26step/s]INFO 2026-05-20 22:31:37 ot_train.py:488 step:6K smpl:364K ep:901 epch:1.33 loss:0.008 grdn:0.168 lr:4.4e-05 updt_s:0.090 data_s:0.006
+Training:  23%|████████▉                              | 5699/25000 [09:54<29:08, 11.04step/s]INFO 2026-05-20 22:31:39 ot_train.py:488 step:6K smpl:365K ep:904 epch:1.34 loss:0.008 grdn:0.166 lr:4.4e-05 updt_s:0.091 data_s:0.008
+Training:  23%|████████▉                              | 5720/25000 [09:56<28:24, 11.31step/s]INFO 2026-05-20 22:31:41 ot_train.py:488 step:6K smpl:366K ep:907 epch:1.34 loss:0.009 grdn:0.165 lr:4.4e-05 updt_s:0.087 data_s:0.008
+Training:  23%|████████▉                              | 5740/25000 [09:58<32:42,  9.81step/s]INFO 2026-05-20 22:31:43 ot_train.py:488 step:6K smpl:367K ep:911 epch:1.35 loss:0.008 grdn:0.156 lr:4.4e-05 updt_s:0.092 data_s:0.009
+Training:  23%|████████▉                              | 5759/25000 [10:00<32:36,  9.83step/s]INFO 2026-05-20 22:31:45 ot_train.py:488 step:6K smpl:369K ep:914 epch:1.35 loss:0.010 grdn:0.179 lr:4.4e-05 updt_s:0.086 data_s:0.011
+Training:  23%|█████████                              | 5779/25000 [10:02<30:12, 10.60step/s]INFO 2026-05-20 22:31:47 ot_train.py:488 step:6K smpl:370K ep:917 epch:1.36 loss:0.009 grdn:0.185 lr:4.4e-05 updt_s:0.089 data_s:0.008
+Training:  23%|█████████                              | 5799/25000 [10:04<32:18,  9.90step/s]INFO 2026-05-20 22:31:49 ot_train.py:488 step:6K smpl:371K ep:920 epch:1.36 loss:0.008 grdn:0.171 lr:4.4e-05 updt_s:0.087 data_s:0.012
+Training:  23%|█████████                              | 5819/25000 [10:06<30:45, 10.39step/s]INFO 2026-05-20 22:31:51 ot_train.py:488 step:6K smpl:372K ep:923 epch:1.37 loss:0.008 grdn:0.157 lr:4.4e-05 updt_s:0.086 data_s:0.011
+Training:  23%|█████████                              | 5839/25000 [10:08<29:43, 10.74step/s]INFO 2026-05-20 22:31:53 ot_train.py:488 step:6K smpl:374K ep:927 epch:1.37 loss:0.009 grdn:0.164 lr:4.4e-05 updt_s:0.089 data_s:0.007
+Training:  23%|█████████▏                             | 5859/25000 [10:10<32:13,  9.90step/s]INFO 2026-05-20 22:31:55 ot_train.py:488 step:6K smpl:375K ep:930 epch:1.38 loss:0.009 grdn:0.170 lr:4.4e-05 updt_s:0.094 data_s:0.009
+Training:  24%|█████████▏                             | 5879/25000 [10:12<32:20,  9.86step/s]INFO 2026-05-20 22:31:57 ot_train.py:488 step:6K smpl:376K ep:933 epch:1.38 loss:0.009 grdn:0.187 lr:4.4e-05 updt_s:0.088 data_s:0.008
+Training:  24%|█████████▏                             | 5899/25000 [10:14<30:39, 10.39step/s]INFO 2026-05-20 22:31:59 ot_train.py:488 step:6K smpl:378K ep:936 epch:1.38 loss:0.009 grdn:0.174 lr:4.4e-05 updt_s:0.085 data_s:0.011
+Training:  24%|█████████▏                             | 5919/25000 [10:16<31:58,  9.95step/s]INFO 2026-05-20 22:32:01 ot_train.py:488 step:6K smpl:379K ep:939 epch:1.39 loss:0.009 grdn:0.177 lr:4.4e-05 updt_s:0.087 data_s:0.009
+Training:  24%|█████████▎                             | 5939/25000 [10:18<30:57, 10.26step/s]INFO 2026-05-20 22:32:03 ot_train.py:488 step:6K smpl:380K ep:942 epch:1.39 loss:0.009 grdn:0.181 lr:4.4e-05 updt_s:0.089 data_s:0.006
+Training:  24%|█████████▎                             | 5959/25000 [10:20<30:06, 10.54step/s]INFO 2026-05-20 22:32:05 ot_train.py:488 step:6K smpl:381K ep:946 epch:1.40 loss:0.010 grdn:0.189 lr:4.3e-05 updt_s:0.085 data_s:0.013
+Training:  24%|█████████▎                             | 5980/25000 [10:22<32:40,  9.70step/s]INFO 2026-05-20 22:32:07 ot_train.py:488 step:6K smpl:383K ep:949 epch:1.40 loss:0.009 grdn:0.176 lr:4.3e-05 updt_s:0.088 data_s:0.009
+Training:  24%|█████████▎                             | 5999/25000 [10:24<31:01, 10.21step/s]INFO 2026-05-20 22:32:09 ot_train.py:488 step:6K smpl:384K ep:952 epch:1.41 loss:0.009 grdn:0.180 lr:4.3e-05 updt_s:0.089 data_s:0.008
+INFO 2026-05-20 22:32:09 ot_train.py:502 Checkpoint policy after step 6000
+Training:  24%|█████████▍                             | 6019/25000 [10:34<42:05,  7.51step/s]INFO 2026-05-20 22:32:18 ot_train.py:488 step:6K smpl:385K ep:955 epch:1.41 loss:0.009 grdn:0.182 lr:4.3e-05 updt_s:0.070 data_s:0.006
+Training:  24%|█████████▍                             | 6039/25000 [10:36<30:07, 10.49step/s]INFO 2026-05-20 22:32:21 ot_train.py:488 step:6K smpl:387K ep:958 epch:1.42 loss:0.008 grdn:0.171 lr:4.3e-05 updt_s:0.103 data_s:0.009
+Training:  24%|█████████▍                             | 6059/25000 [10:38<30:32, 10.34step/s]INFO 2026-05-20 22:32:23 ot_train.py:488 step:6K smpl:388K ep:961 epch:1.42 loss:0.008 grdn:0.164 lr:4.3e-05 updt_s:0.084 data_s:0.006
+Training:  24%|█████████▍                             | 6079/25000 [10:40<31:28, 10.02step/s]INFO 2026-05-20 22:32:24 ot_train.py:488 step:6K smpl:389K ep:965 epch:1.43 loss:0.008 grdn:0.156 lr:4.3e-05 updt_s:0.086 data_s:0.010
+Training:  24%|█████████▌                             | 6099/25000 [10:42<28:36, 11.01step/s]INFO 2026-05-20 22:32:26 ot_train.py:488 step:6K smpl:390K ep:968 epch:1.43 loss:0.008 grdn:0.168 lr:4.3e-05 updt_s:0.089 data_s:0.007
+Training:  24%|█████████▌                             | 6119/25000 [10:44<29:07, 10.80step/s]INFO 2026-05-20 22:32:28 ot_train.py:488 step:6K smpl:392K ep:971 epch:1.44 loss:0.009 grdn:0.189 lr:4.3e-05 updt_s:0.088 data_s:0.006
+Training:  25%|█████████▌                             | 6139/25000 [10:46<30:52, 10.18step/s]INFO 2026-05-20 22:32:30 ot_train.py:488 step:6K smpl:393K ep:974 epch:1.44 loss:0.007 grdn:0.159 lr:4.3e-05 updt_s:0.087 data_s:0.009
+Training:  25%|█████████▌                             | 6159/25000 [10:48<31:13, 10.05step/s]INFO 2026-05-20 22:32:32 ot_train.py:488 step:6K smpl:394K ep:977 epch:1.45 loss:0.008 grdn:0.171 lr:4.3e-05 updt_s:0.087 data_s:0.008
+Training:  25%|█████████▋                             | 6179/25000 [10:50<28:08, 11.15step/s]INFO 2026-05-20 22:32:34 ot_train.py:488 step:6K smpl:396K ep:980 epch:1.45 loss:0.008 grdn:0.159 lr:4.3e-05 updt_s:0.088 data_s:0.008
+Training:  25%|█████████▋                             | 6200/25000 [10:52<32:38,  9.60step/s]INFO 2026-05-20 22:32:36 ot_train.py:488 step:6K smpl:397K ep:984 epch:1.46 loss:0.008 grdn:0.170 lr:4.3e-05 updt_s:0.093 data_s:0.007
+Training:  25%|█████████▋                             | 6219/25000 [10:54<28:43, 10.90step/s]INFO 2026-05-20 22:32:38 ot_train.py:488 step:6K smpl:398K ep:987 epch:1.46 loss:0.010 grdn:0.184 lr:4.3e-05 updt_s:0.083 data_s:0.008
+Training:  25%|█████████▋                             | 6240/25000 [10:56<30:11, 10.36step/s]INFO 2026-05-20 22:32:40 ot_train.py:488 step:6K smpl:399K ep:990 epch:1.46 loss:0.009 grdn:0.170 lr:4.3e-05 updt_s:0.090 data_s:0.011
+Training:  25%|█████████▊                             | 6260/25000 [10:58<29:37, 10.54step/s]INFO 2026-05-20 22:32:42 ot_train.py:488 step:6K smpl:401K ep:993 epch:1.47 loss:0.009 grdn:0.179 lr:4.3e-05 updt_s:0.089 data_s:0.007
+Training:  25%|█████████▊                             | 6279/25000 [11:00<31:17,  9.97step/s]INFO 2026-05-20 22:32:44 ot_train.py:488 step:6K smpl:402K ep:996 epch:1.47 loss:0.008 grdn:0.160 lr:4.3e-05 updt_s:0.090 data_s:0.008
+Training:  25%|█████████▊                             | 6299/25000 [11:02<31:44,  9.82step/s]INFO 2026-05-20 22:32:46 ot_train.py:488 step:6K smpl:403K ep:999 epch:1.48 loss:0.008 grdn:0.163 lr:4.3e-05 updt_s:0.093 data_s:0.006
+Training:  25%|█████████▊                             | 6320/25000 [11:04<30:30, 10.20step/s]INFO 2026-05-20 22:32:48 ot_train.py:488 step:6K smpl:404K ep:1K epch:1.48 loss:0.009 grdn:0.169 lr:4.3e-05 updt_s:0.081 data_s:0.014
+Training:  25%|█████████▉                             | 6340/25000 [11:06<32:14,  9.65step/s]INFO 2026-05-20 22:32:50 ot_train.py:488 step:6K smpl:406K ep:1K epch:1.49 loss:0.009 grdn:0.161 lr:4.3e-05 updt_s:0.091 data_s:0.011
+Training:  25%|█████████▉                             | 6360/25000 [11:08<31:04, 10.00step/s]INFO 2026-05-20 22:32:52 ot_train.py:488 step:6K smpl:407K ep:1K epch:1.49 loss:0.009 grdn:0.175 lr:4.3e-05 updt_s:0.088 data_s:0.009
+Training:  26%|█████████▉                             | 6380/25000 [11:10<30:33, 10.15step/s]INFO 2026-05-20 22:32:54 ot_train.py:488 step:6K smpl:408K ep:1K epch:1.50 loss:0.008 grdn:0.162 lr:4.3e-05 updt_s:0.090 data_s:0.008
+Training:  26%|█████████▉                             | 6399/25000 [11:12<30:06, 10.30step/s]INFO 2026-05-20 22:32:56 ot_train.py:488 step:6K smpl:410K ep:1K epch:1.50 loss:0.008 grdn:0.150 lr:4.3e-05 updt_s:0.089 data_s:0.010
+Training:  26%|██████████                             | 6419/25000 [11:13<30:28, 10.16step/s]INFO 2026-05-20 22:32:58 ot_train.py:488 step:6K smpl:411K ep:1K epch:1.51 loss:0.009 grdn:0.165 lr:4.2e-05 updt_s:0.087 data_s:0.008
+Training:  26%|██████████                             | 6440/25000 [11:16<32:08,  9.63step/s]INFO 2026-05-20 22:33:00 ot_train.py:488 step:6K smpl:412K ep:1K epch:1.51 loss:0.008 grdn:0.173 lr:4.2e-05 updt_s:0.092 data_s:0.010
+Training:  26%|██████████                             | 6459/25000 [11:17<29:15, 10.56step/s]INFO 2026-05-20 22:33:02 ot_train.py:488 step:6K smpl:413K ep:1K epch:1.52 loss:0.008 grdn:0.166 lr:4.2e-05 updt_s:0.087 data_s:0.009
+Training:  26%|██████████                             | 6479/25000 [11:19<30:17, 10.19step/s]INFO 2026-05-20 22:33:04 ot_train.py:488 step:6K smpl:415K ep:1K epch:1.52 loss:0.009 grdn:0.173 lr:4.2e-05 updt_s:0.088 data_s:0.011
+Training:  26%|██████████▏                            | 6499/25000 [11:21<29:30, 10.45step/s]INFO 2026-05-20 22:33:06 ot_train.py:488 step:6K smpl:416K ep:1K epch:1.53 loss:0.008 grdn:0.177 lr:4.2e-05 updt_s:0.090 data_s:0.008
+Training:  26%|██████████▏                            | 6519/25000 [11:23<28:53, 10.66step/s]INFO 2026-05-20 22:33:08 ot_train.py:488 step:7K smpl:417K ep:1K epch:1.53 loss:0.009 grdn:0.171 lr:4.2e-05 updt_s:0.084 data_s:0.009
+Training:  26%|██████████▏                            | 6539/25000 [11:25<30:05, 10.22step/s]INFO 2026-05-20 22:33:10 ot_train.py:488 step:7K smpl:419K ep:1K epch:1.53 loss:0.008 grdn:0.156 lr:4.2e-05 updt_s:0.090 data_s:0.006
+Training:  26%|██████████▏                            | 6559/25000 [11:27<29:11, 10.53step/s]INFO 2026-05-20 22:33:12 ot_train.py:488 step:7K smpl:420K ep:1K epch:1.54 loss:0.009 grdn:0.179 lr:4.2e-05 updt_s:0.088 data_s:0.006
+Training:  26%|██████████▎                            | 6579/25000 [11:29<29:16, 10.49step/s]INFO 2026-05-20 22:33:14 ot_train.py:488 step:7K smpl:421K ep:1K epch:1.54 loss:0.010 grdn:0.196 lr:4.2e-05 updt_s:0.090 data_s:0.008
+Training:  26%|██████████▎                            | 6599/25000 [11:31<30:08, 10.18step/s]INFO 2026-05-20 22:33:16 ot_train.py:488 step:7K smpl:422K ep:1K epch:1.55 loss:0.009 grdn:0.181 lr:4.2e-05 updt_s:0.090 data_s:0.008
+Training:  26%|██████████▎                            | 6620/25000 [11:33<30:42,  9.97step/s]INFO 2026-05-20 22:33:18 ot_train.py:488 step:7K smpl:424K ep:1K epch:1.55 loss:0.009 grdn:0.170 lr:4.2e-05 updt_s:0.090 data_s:0.011
+Training:  27%|██████████▎                            | 6640/25000 [11:35<29:16, 10.45step/s]INFO 2026-05-20 22:33:20 ot_train.py:488 step:7K smpl:425K ep:1K epch:1.56 loss:0.008 grdn:0.163 lr:4.2e-05 updt_s:0.092 data_s:0.006
+Training:  27%|██████████▍                            | 6660/25000 [11:37<30:40,  9.96step/s]INFO 2026-05-20 22:33:22 ot_train.py:488 step:7K smpl:426K ep:1K epch:1.56 loss:0.008 grdn:0.169 lr:4.2e-05 updt_s:0.088 data_s:0.012
+Training:  27%|██████████▍                            | 6680/25000 [11:39<32:14,  9.47step/s]INFO 2026-05-20 22:33:24 ot_train.py:488 step:7K smpl:428K ep:1K epch:1.57 loss:0.009 grdn:0.187 lr:4.2e-05 updt_s:0.091 data_s:0.010
+Training:  27%|██████████▍                            | 6699/25000 [11:41<29:20, 10.39step/s]INFO 2026-05-20 22:33:26 ot_train.py:488 step:7K smpl:429K ep:1K epch:1.57 loss:0.009 grdn:0.171 lr:4.2e-05 updt_s:0.089 data_s:0.010
+Training:  27%|██████████▍                            | 6719/25000 [11:43<29:46, 10.23step/s]INFO 2026-05-20 22:33:28 ot_train.py:488 step:7K smpl:430K ep:1K epch:1.58 loss:0.008 grdn:0.162 lr:4.2e-05 updt_s:0.085 data_s:0.011
+Training:  27%|██████████▌                            | 6739/25000 [11:45<30:07, 10.10step/s]INFO 2026-05-20 22:33:30 ot_train.py:488 step:7K smpl:431K ep:1K epch:1.58 loss:0.008 grdn:0.160 lr:4.2e-05 updt_s:0.092 data_s:0.009
+Training:  27%|██████████▌                            | 6759/25000 [11:47<29:00, 10.48step/s]INFO 2026-05-20 22:33:32 ot_train.py:488 step:7K smpl:433K ep:1K epch:1.59 loss:0.009 grdn:0.159 lr:4.2e-05 updt_s:0.090 data_s:0.006
+Training:  27%|██████████▌                            | 6779/25000 [11:49<31:24,  9.67step/s]INFO 2026-05-20 22:33:34 ot_train.py:488 step:7K smpl:434K ep:1K epch:1.59 loss:0.008 grdn:0.163 lr:4.2e-05 updt_s:0.089 data_s:0.008
+Training:  27%|██████████▌                            | 6800/25000 [11:51<29:44, 10.20step/s]INFO 2026-05-20 22:33:36 ot_train.py:488 step:7K smpl:435K ep:1K epch:1.60 loss:0.007 grdn:0.160 lr:4.2e-05 updt_s:0.092 data_s:0.006
+Training:  27%|██████████▋                            | 6819/25000 [11:53<29:55, 10.13step/s]INFO 2026-05-20 22:33:38 ot_train.py:488 step:7K smpl:436K ep:1K epch:1.60 loss:0.009 grdn:0.183 lr:4.2e-05 updt_s:0.093 data_s:0.008
+Training:  27%|██████████▋                            | 6840/25000 [11:55<34:53,  8.67step/s]INFO 2026-05-20 22:33:40 ot_train.py:488 step:7K smpl:438K ep:1K epch:1.61 loss:0.009 grdn:0.161 lr:4.2e-05 updt_s:0.095 data_s:0.008
+Training:  27%|██████████▋                            | 6859/25000 [11:57<28:47, 10.50step/s]INFO 2026-05-20 22:33:42 ot_train.py:488 step:7K smpl:439K ep:1K epch:1.61 loss:0.009 grdn:0.183 lr:4.1e-05 updt_s:0.087 data_s:0.006
+Training:  28%|██████████▋                            | 6879/25000 [11:59<28:52, 10.46step/s]INFO 2026-05-20 22:33:44 ot_train.py:488 step:7K smpl:440K ep:1K epch:1.61 loss:0.009 grdn:0.179 lr:4.1e-05 updt_s:0.094 data_s:0.007
+Training:  28%|██████████▊                            | 6899/25000 [12:01<29:57, 10.07step/s]INFO 2026-05-20 22:33:46 ot_train.py:488 step:7K smpl:442K ep:1K epch:1.62 loss:0.007 grdn:0.159 lr:4.1e-05 updt_s:0.087 data_s:0.010
+Training:  28%|██████████▊                            | 6920/25000 [12:03<29:21, 10.26step/s]INFO 2026-05-20 22:33:48 ot_train.py:488 step:7K smpl:443K ep:1K epch:1.62 loss:0.008 grdn:0.166 lr:4.1e-05 updt_s:0.090 data_s:0.010
+Training:  28%|██████████▊                            | 6940/25000 [12:05<29:15, 10.29step/s]INFO 2026-05-20 22:33:50 ot_train.py:488 step:7K smpl:444K ep:1K epch:1.63 loss:0.009 grdn:0.202 lr:4.1e-05 updt_s:0.091 data_s:0.006
+Training:  28%|██████████▊                            | 6960/25000 [12:07<28:19, 10.61step/s]INFO 2026-05-20 22:33:52 ot_train.py:488 step:7K smpl:445K ep:1K epch:1.63 loss:0.008 grdn:0.179 lr:4.1e-05 updt_s:0.091 data_s:0.006
+Training:  28%|██████████▉                            | 6980/25000 [12:09<27:06, 11.08step/s]INFO 2026-05-20 22:33:53 ot_train.py:488 step:7K smpl:447K ep:1K epch:1.64 loss:0.009 grdn:0.188 lr:4.1e-05 updt_s:0.085 data_s:0.008
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+Training:  28%|██████████▉                            | 7020/25000 [12:13<31:53,  9.40step/s]INFO 2026-05-20 22:33:58 ot_train.py:488 step:7K smpl:449K ep:1K epch:1.65 loss:0.008 grdn:0.157 lr:4.1e-05 updt_s:0.086 data_s:0.012
+Training:  28%|██████████▉                            | 7040/25000 [12:15<28:34, 10.47step/s]INFO 2026-05-20 22:34:00 ot_train.py:488 step:7K smpl:451K ep:1K epch:1.65 loss:0.009 grdn:0.176 lr:4.1e-05 updt_s:0.088 data_s:0.008
+Training:  28%|███████████                            | 7060/25000 [12:17<30:01,  9.96step/s]INFO 2026-05-20 22:34:02 ot_train.py:488 step:7K smpl:452K ep:1K epch:1.66 loss:0.008 grdn:0.167 lr:4.1e-05 updt_s:0.093 data_s:0.008
+Training:  28%|███████████                            | 7080/25000 [12:19<30:10,  9.90step/s]INFO 2026-05-20 22:34:04 ot_train.py:488 step:7K smpl:453K ep:1K epch:1.66 loss:0.009 grdn:0.175 lr:4.1e-05 updt_s:0.088 data_s:0.011
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+Training:  28%|███████████                            | 7120/25000 [12:23<30:03,  9.91step/s]INFO 2026-05-20 22:34:07 ot_train.py:488 step:7K smpl:456K ep:1K epch:1.67 loss:0.009 grdn:0.178 lr:4.1e-05 updt_s:0.088 data_s:0.008
+Training:  29%|███████████▏                           | 7139/25000 [12:25<29:08, 10.22step/s]INFO 2026-05-20 22:34:09 ot_train.py:488 step:7K smpl:457K ep:1K epch:1.68 loss:0.008 grdn:0.179 lr:4.1e-05 updt_s:0.085 data_s:0.011
+Training:  29%|███████████▏                           | 7159/25000 [12:27<30:03,  9.89step/s]INFO 2026-05-20 22:34:11 ot_train.py:488 step:7K smpl:458K ep:1K epch:1.68 loss:0.008 grdn:0.150 lr:4.1e-05 updt_s:0.086 data_s:0.010
+Training:  29%|███████████▏                           | 7179/25000 [12:29<29:22, 10.11step/s]INFO 2026-05-20 22:34:13 ot_train.py:488 step:7K smpl:460K ep:1K epch:1.69 loss:0.009 grdn:0.174 lr:4.1e-05 updt_s:0.091 data_s:0.008
+Training:  29%|███████████▏                           | 7199/25000 [12:31<29:40, 10.00step/s]INFO 2026-05-20 22:34:15 ot_train.py:488 step:7K smpl:461K ep:1K epch:1.69 loss:0.010 grdn:0.190 lr:4.1e-05 updt_s:0.088 data_s:0.007
+Training:  29%|███████████▎                           | 7220/25000 [12:33<31:12,  9.50step/s]INFO 2026-05-20 22:34:18 ot_train.py:488 step:7K smpl:462K ep:1K epch:1.69 loss:0.009 grdn:0.179 lr:4.1e-05 updt_s:0.093 data_s:0.009
+Training:  29%|███████████▎                           | 7240/25000 [12:35<28:32, 10.37step/s]INFO 2026-05-20 22:34:19 ot_train.py:488 step:7K smpl:463K ep:1K epch:1.70 loss:0.008 grdn:0.157 lr:4.1e-05 updt_s:0.085 data_s:0.008
+Training:  29%|███████████▎                           | 7259/25000 [12:37<32:14,  9.17step/s]INFO 2026-05-20 22:34:22 ot_train.py:488 step:7K smpl:465K ep:1K epch:1.70 loss:0.008 grdn:0.178 lr:4.0e-05 updt_s:0.090 data_s:0.015
+Training:  29%|███████████▎                           | 7280/25000 [12:39<28:56, 10.21step/s]INFO 2026-05-20 22:34:23 ot_train.py:488 step:7K smpl:466K ep:1K epch:1.71 loss:0.009 grdn:0.167 lr:4.0e-05 updt_s:0.089 data_s:0.006
+Training:  29%|███████████▍                           | 7300/25000 [12:41<30:28,  9.68step/s]INFO 2026-05-20 22:34:25 ot_train.py:488 step:7K smpl:467K ep:1K epch:1.71 loss:0.008 grdn:0.166 lr:4.0e-05 updt_s:0.089 data_s:0.006
+Training:  29%|███████████▍                           | 7320/25000 [12:43<30:13,  9.75step/s]INFO 2026-05-20 22:34:27 ot_train.py:488 step:7K smpl:468K ep:1K epch:1.72 loss:0.009 grdn:0.170 lr:4.0e-05 updt_s:0.089 data_s:0.007
+Training:  29%|███████████▍                           | 7340/25000 [12:45<29:07, 10.11step/s]INFO 2026-05-20 22:34:29 ot_train.py:488 step:7K smpl:470K ep:1K epch:1.72 loss:0.008 grdn:0.166 lr:4.0e-05 updt_s:0.088 data_s:0.008
+Training:  29%|███████████▍                           | 7360/25000 [12:47<29:06, 10.10step/s]INFO 2026-05-20 22:34:31 ot_train.py:488 step:7K smpl:471K ep:1K epch:1.73 loss:0.009 grdn:0.174 lr:4.0e-05 updt_s:0.089 data_s:0.010
+Training:  30%|███████████▌                           | 7380/25000 [12:49<28:08, 10.44step/s]INFO 2026-05-20 22:34:33 ot_train.py:488 step:7K smpl:472K ep:1K epch:1.73 loss:0.008 grdn:0.181 lr:4.0e-05 updt_s:0.091 data_s:0.008
+Training:  30%|███████████▌                           | 7400/25000 [12:51<29:19, 10.00step/s]INFO 2026-05-20 22:34:35 ot_train.py:488 step:7K smpl:474K ep:1K epch:1.74 loss:0.008 grdn:0.169 lr:4.0e-05 updt_s:0.088 data_s:0.009
+Training:  30%|███████████▌                           | 7420/25000 [12:53<30:15,  9.68step/s]INFO 2026-05-20 22:34:37 ot_train.py:488 step:7K smpl:475K ep:1K epch:1.74 loss:0.008 grdn:0.152 lr:4.0e-05 updt_s:0.090 data_s:0.008
+Training:  30%|███████████▌                           | 7440/25000 [12:55<29:01, 10.09step/s]INFO 2026-05-20 22:34:39 ot_train.py:488 step:7K smpl:476K ep:1K epch:1.75 loss:0.008 grdn:0.156 lr:4.0e-05 updt_s:0.087 data_s:0.006
+Training:  30%|███████████▋                           | 7459/25000 [12:57<29:19,  9.97step/s]INFO 2026-05-20 22:34:41 ot_train.py:488 step:7K smpl:477K ep:1K epch:1.75 loss:0.008 grdn:0.182 lr:4.0e-05 updt_s:0.091 data_s:0.006
+Training:  30%|███████████▋                           | 7479/25000 [12:59<28:38, 10.20step/s]INFO 2026-05-20 22:34:43 ot_train.py:488 step:7K smpl:479K ep:1K epch:1.76 loss:0.009 grdn:0.179 lr:4.0e-05 updt_s:0.089 data_s:0.010
+Training:  30%|███████████▋                           | 7499/25000 [13:01<34:17,  8.50step/s]INFO 2026-05-20 22:34:45 ot_train.py:488 step:8K smpl:480K ep:1K epch:1.76 loss:0.009 grdn:0.180 lr:4.0e-05 updt_s:0.091 data_s:0.010
+Training:  30%|███████████▋                           | 7519/25000 [13:03<28:37, 10.18step/s]INFO 2026-05-20 22:34:47 ot_train.py:488 step:8K smpl:481K ep:1K epch:1.76 loss:0.008 grdn:0.174 lr:4.0e-05 updt_s:0.084 data_s:0.009
+Training:  30%|███████████▊                           | 7540/25000 [13:05<26:09, 11.12step/s]INFO 2026-05-20 22:34:49 ot_train.py:488 step:8K smpl:483K ep:1K epch:1.77 loss:0.008 grdn:0.171 lr:4.0e-05 updt_s:0.081 data_s:0.013
+Training:  30%|███████████▊                           | 7559/25000 [13:07<30:26,  9.55step/s]INFO 2026-05-20 22:34:51 ot_train.py:488 step:8K smpl:484K ep:1K epch:1.77 loss:0.008 grdn:0.179 lr:4.0e-05 updt_s:0.094 data_s:0.008
+Training:  30%|███████████▊                           | 7580/25000 [13:09<28:21, 10.24step/s]INFO 2026-05-20 22:34:53 ot_train.py:488 step:8K smpl:485K ep:1K epch:1.78 loss:0.008 grdn:0.162 lr:4.0e-05 updt_s:0.092 data_s:0.006
+Training:  30%|███████████▊                           | 7600/25000 [13:11<29:19,  9.89step/s]INFO 2026-05-20 22:34:55 ot_train.py:488 step:8K smpl:486K ep:1K epch:1.78 loss:0.008 grdn:0.169 lr:4.0e-05 updt_s:0.090 data_s:0.008
+Training:  30%|███████████▉                           | 7619/25000 [13:13<26:58, 10.74step/s]INFO 2026-05-20 22:34:57 ot_train.py:488 step:8K smpl:488K ep:1K epch:1.79 loss:0.007 grdn:0.170 lr:4.0e-05 updt_s:0.088 data_s:0.006
+Training:  31%|███████████▉                           | 7639/25000 [13:15<27:25, 10.55step/s]INFO 2026-05-20 22:34:59 ot_train.py:488 step:8K smpl:489K ep:1K epch:1.79 loss:0.008 grdn:0.175 lr:4.0e-05 updt_s:0.089 data_s:0.008
+Training:  31%|███████████▉                           | 7659/25000 [13:17<28:25, 10.17step/s]INFO 2026-05-20 22:35:01 ot_train.py:488 step:8K smpl:490K ep:1K epch:1.80 loss:0.009 grdn:0.195 lr:3.9e-05 updt_s:0.093 data_s:0.009
+Training:  31%|███████████▉                           | 7679/25000 [13:19<29:00,  9.95step/s]INFO 2026-05-20 22:35:03 ot_train.py:488 step:8K smpl:492K ep:1K epch:1.80 loss:0.007 grdn:0.161 lr:3.9e-05 updt_s:0.088 data_s:0.008
+Training:  31%|████████████                           | 7699/25000 [13:21<28:26, 10.14step/s]INFO 2026-05-20 22:35:05 ot_train.py:488 step:8K smpl:493K ep:1K epch:1.81 loss:0.008 grdn:0.163 lr:3.9e-05 updt_s:0.090 data_s:0.008
+Training:  31%|████████████                           | 7720/25000 [13:23<27:39, 10.41step/s]INFO 2026-05-20 22:35:07 ot_train.py:488 step:8K smpl:494K ep:1K epch:1.81 loss:0.008 grdn:0.158 lr:3.9e-05 updt_s:0.089 data_s:0.008
+Training:  31%|████████████                           | 7740/25000 [13:25<29:51,  9.63step/s]INFO 2026-05-20 22:35:09 ot_train.py:488 step:8K smpl:495K ep:1K epch:1.82 loss:0.008 grdn:0.175 lr:3.9e-05 updt_s:0.090 data_s:0.011
+Training:  31%|████████████                           | 7760/25000 [13:27<28:03, 10.24step/s]INFO 2026-05-20 22:35:11 ot_train.py:488 step:8K smpl:497K ep:1K epch:1.82 loss:0.009 grdn:0.182 lr:3.9e-05 updt_s:0.085 data_s:0.011
+Training:  31%|████████████▏                          | 7780/25000 [13:29<26:19, 10.90step/s]INFO 2026-05-20 22:35:13 ot_train.py:488 step:8K smpl:498K ep:1K epch:1.83 loss:0.009 grdn:0.176 lr:3.9e-05 updt_s:0.088 data_s:0.008
+Training:  31%|████████████▏                          | 7800/25000 [13:31<28:21, 10.11step/s]INFO 2026-05-20 22:35:15 ot_train.py:488 step:8K smpl:499K ep:1K epch:1.83 loss:0.008 grdn:0.173 lr:3.9e-05 updt_s:0.094 data_s:0.007
+Training:  31%|████████████▏                          | 7820/25000 [13:33<27:25, 10.44step/s]INFO 2026-05-20 22:35:17 ot_train.py:488 step:8K smpl:500K ep:1K epch:1.84 loss:0.008 grdn:0.174 lr:3.9e-05 updt_s:0.088 data_s:0.006
+Training:  31%|████████████▏                          | 7839/25000 [13:34<27:10, 10.52step/s]INFO 2026-05-20 22:35:19 ot_train.py:488 step:8K smpl:502K ep:1K epch:1.84 loss:0.008 grdn:0.176 lr:3.9e-05 updt_s:0.094 data_s:0.006
+Training:  31%|████████████▎                          | 7859/25000 [13:36<29:01,  9.85step/s]INFO 2026-05-20 22:35:21 ot_train.py:488 step:8K smpl:503K ep:1K epch:1.84 loss:0.010 grdn:0.189 lr:3.9e-05 updt_s:0.086 data_s:0.011
+Training:  32%|████████████▎                          | 7879/25000 [13:38<27:15, 10.47step/s]INFO 2026-05-20 22:35:23 ot_train.py:488 step:8K smpl:504K ep:1K epch:1.85 loss:0.008 grdn:0.164 lr:3.9e-05 updt_s:0.088 data_s:0.008
+Training:  32%|████████████▎                          | 7900/25000 [13:41<28:56,  9.85step/s]INFO 2026-05-20 22:35:25 ot_train.py:488 step:8K smpl:506K ep:1K epch:1.85 loss:0.009 grdn:0.171 lr:3.9e-05 updt_s:0.091 data_s:0.009
+Training:  32%|████████████▎                          | 7920/25000 [13:43<28:16, 10.07step/s]INFO 2026-05-20 22:35:27 ot_train.py:488 step:8K smpl:507K ep:1K epch:1.86 loss:0.009 grdn:0.164 lr:3.9e-05 updt_s:0.089 data_s:0.010
+Training:  32%|████████████▍                          | 7940/25000 [13:44<26:48, 10.61step/s]INFO 2026-05-20 22:35:29 ot_train.py:488 step:8K smpl:508K ep:1K epch:1.86 loss:0.008 grdn:0.162 lr:3.9e-05 updt_s:0.085 data_s:0.011
+Training:  32%|████████████▍                          | 7960/25000 [13:47<29:11,  9.73step/s]INFO 2026-05-20 22:35:31 ot_train.py:488 step:8K smpl:509K ep:1K epch:1.87 loss:0.009 grdn:0.172 lr:3.9e-05 updt_s:0.092 data_s:0.011
+Training:  32%|████████████▍                          | 7980/25000 [13:48<29:29,  9.62step/s]INFO 2026-05-20 22:35:33 ot_train.py:488 step:8K smpl:511K ep:1K epch:1.87 loss:0.008 grdn:0.171 lr:3.9e-05 updt_s:0.085 data_s:0.008
+Training:  32%|████████████▍                          | 8000/25000 [13:50<26:46, 10.58step/s]INFO 2026-05-20 22:35:35 ot_train.py:488 step:8K smpl:512K ep:1K epch:1.88 loss:0.009 grdn:0.185 lr:3.9e-05 updt_s:0.087 data_s:0.011
+INFO 2026-05-20 22:35:35 ot_train.py:502 Checkpoint policy after step 8000
+Training:  32%|████████████▌                          | 8020/25000 [14:00<37:07,  7.62step/s]INFO 2026-05-20 22:35:44 ot_train.py:488 step:8K smpl:513K ep:1K epch:1.88 loss:0.008 grdn:0.172 lr:3.9e-05 updt_s:0.068 data_s:0.008
+Training:  32%|████████████▌                          | 8040/25000 [14:02<28:06, 10.06step/s]INFO 2026-05-20 22:35:46 ot_train.py:488 step:8K smpl:515K ep:1K epch:1.89 loss:0.008 grdn:0.155 lr:3.8e-05 updt_s:0.091 data_s:0.011
+Training:  32%|████████████▌                          | 8060/25000 [14:04<28:39,  9.85step/s]INFO 2026-05-20 22:35:48 ot_train.py:488 step:8K smpl:516K ep:1K epch:1.89 loss:0.007 grdn:0.169 lr:3.8e-05 updt_s:0.088 data_s:0.008
+Training:  32%|████████████▌                          | 8079/25000 [14:06<27:49, 10.13step/s]INFO 2026-05-20 22:35:50 ot_train.py:488 step:8K smpl:517K ep:1K epch:1.90 loss:0.009 grdn:0.172 lr:3.8e-05 updt_s:0.086 data_s:0.009
+Training:  32%|████████████▋                          | 8100/25000 [14:08<27:15, 10.33step/s]INFO 2026-05-20 22:35:52 ot_train.py:488 step:8K smpl:518K ep:1K epch:1.90 loss:0.008 grdn:0.153 lr:3.8e-05 updt_s:0.083 data_s:0.014
+Training:  32%|████████████▋                          | 8120/25000 [14:10<27:42, 10.16step/s]INFO 2026-05-20 22:35:54 ot_train.py:488 step:8K smpl:520K ep:1K epch:1.91 loss:0.009 grdn:0.188 lr:3.8e-05 updt_s:0.090 data_s:0.007
+Training:  33%|████████████▋                          | 8140/25000 [14:12<27:48, 10.10step/s]INFO 2026-05-20 22:35:56 ot_train.py:488 step:8K smpl:521K ep:1K epch:1.91 loss:0.008 grdn:0.162 lr:3.8e-05 updt_s:0.087 data_s:0.010
+Training:  33%|████████████▋                          | 8160/25000 [14:14<27:39, 10.14step/s]INFO 2026-05-20 22:35:58 ot_train.py:488 step:8K smpl:522K ep:1K epch:1.92 loss:0.008 grdn:0.170 lr:3.8e-05 updt_s:0.087 data_s:0.011
+Training:  33%|████████████▊                          | 8180/25000 [14:16<28:13,  9.93step/s]INFO 2026-05-20 22:36:00 ot_train.py:488 step:8K smpl:524K ep:1K epch:1.92 loss:0.007 grdn:0.152 lr:3.8e-05 updt_s:0.090 data_s:0.008
+Training:  33%|████████████▊                          | 8200/25000 [14:18<27:32, 10.17step/s]INFO 2026-05-20 22:36:02 ot_train.py:488 step:8K smpl:525K ep:1K epch:1.92 loss:0.008 grdn:0.169 lr:3.8e-05 updt_s:0.084 data_s:0.010
+Training:  33%|████████████▊                          | 8220/25000 [14:20<26:29, 10.56step/s]INFO 2026-05-20 22:36:04 ot_train.py:488 step:8K smpl:526K ep:1K epch:1.93 loss:0.009 grdn:0.176 lr:3.8e-05 updt_s:0.085 data_s:0.011
+Training:  33%|████████████▊                          | 8240/25000 [14:22<27:46, 10.05step/s]INFO 2026-05-20 22:36:06 ot_train.py:488 step:8K smpl:527K ep:1K epch:1.93 loss:0.008 grdn:0.160 lr:3.8e-05 updt_s:0.087 data_s:0.009
+Training:  33%|████████████▉                          | 8260/25000 [14:24<25:11, 11.08step/s]INFO 2026-05-20 22:36:08 ot_train.py:488 step:8K smpl:529K ep:1K epch:1.94 loss:0.008 grdn:0.169 lr:3.8e-05 updt_s:0.088 data_s:0.007
+Training:  33%|████████████▉                          | 8280/25000 [14:26<27:23, 10.17step/s]INFO 2026-05-20 22:36:10 ot_train.py:488 step:8K smpl:530K ep:1K epch:1.94 loss:0.009 grdn:0.178 lr:3.8e-05 updt_s:0.088 data_s:0.008
+Training:  33%|████████████▉                          | 8300/25000 [14:28<26:47, 10.39step/s]INFO 2026-05-20 22:36:12 ot_train.py:488 step:8K smpl:531K ep:1K epch:1.95 loss:0.007 grdn:0.171 lr:3.8e-05 updt_s:0.088 data_s:0.011
+Training:  33%|████████████▉                          | 8319/25000 [14:30<26:30, 10.49step/s]INFO 2026-05-20 22:36:14 ot_train.py:488 step:8K smpl:532K ep:1K epch:1.95 loss:0.008 grdn:0.166 lr:3.8e-05 updt_s:0.084 data_s:0.021
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+Training:  36%|██████████████▏                        | 9060/25000 [15:46<26:21, 10.08step/s]INFO 2026-05-20 22:37:30 ot_train.py:488 step:9K smpl:580K ep:1K epch:2.13 loss:0.008 grdn:0.173 lr:3.6e-05 updt_s:0.090 data_s:0.008
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+Training:  37%|██████████████▎                        | 9159/25000 [15:56<26:07, 10.10step/s]INFO 2026-05-20 22:37:40 ot_train.py:488 step:9K smpl:586K ep:1K epch:2.15 loss:0.009 grdn:0.169 lr:3.5e-05 updt_s:0.084 data_s:0.013
+Training:  37%|██████████████▎                        | 9180/25000 [15:58<26:12, 10.06step/s]INFO 2026-05-20 22:37:42 ot_train.py:488 step:9K smpl:588K ep:1K epch:2.15 loss:0.008 grdn:0.171 lr:3.5e-05 updt_s:0.089 data_s:0.014
+Training:  37%|██████████████▎                        | 9199/25000 [16:00<23:38, 11.14step/s]INFO 2026-05-20 22:37:44 ot_train.py:488 step:9K smpl:589K ep:1K epch:2.16 loss:0.008 grdn:0.177 lr:3.5e-05 updt_s:0.085 data_s:0.008
+Training:  37%|██████████████▍                        | 9220/25000 [16:02<26:47,  9.82step/s]INFO 2026-05-20 22:37:46 ot_train.py:488 step:9K smpl:590K ep:1K epch:2.16 loss:0.008 grdn:0.164 lr:3.5e-05 updt_s:0.092 data_s:0.012
+Training:  37%|██████████████▍                        | 9240/25000 [16:04<28:38,  9.17step/s]INFO 2026-05-20 22:37:48 ot_train.py:488 step:9K smpl:591K ep:1K epch:2.17 loss:0.009 grdn:0.191 lr:3.5e-05 updt_s:0.089 data_s:0.009
+Training:  37%|██████████████▍                        | 9260/25000 [16:06<26:08, 10.03step/s]INFO 2026-05-20 22:37:50 ot_train.py:488 step:9K smpl:593K ep:1K epch:2.17 loss:0.009 grdn:0.179 lr:3.5e-05 updt_s:0.084 data_s:0.009
+Training:  37%|██████████████▍                        | 9280/25000 [16:08<26:13,  9.99step/s]INFO 2026-05-20 22:37:52 ot_train.py:488 step:9K smpl:594K ep:1K epch:2.18 loss:0.008 grdn:0.184 lr:3.5e-05 updt_s:0.089 data_s:0.009
+Training:  37%|██████████████▌                        | 9300/25000 [16:10<26:12,  9.99step/s]INFO 2026-05-20 22:37:54 ot_train.py:488 step:9K smpl:595K ep:1K epch:2.18 loss:0.009 grdn:0.170 lr:3.5e-05 updt_s:0.085 data_s:0.012
+Training:  37%|██████████████▌                        | 9320/25000 [16:12<25:56, 10.07step/s]INFO 2026-05-20 22:37:56 ot_train.py:488 step:9K smpl:596K ep:1K epch:2.19 loss:0.008 grdn:0.176 lr:3.5e-05 updt_s:0.092 data_s:0.006
+Training:  37%|██████████████▌                        | 9340/25000 [16:14<26:15,  9.94step/s]INFO 2026-05-20 22:37:58 ot_train.py:488 step:9K smpl:598K ep:1K epch:2.19 loss:0.008 grdn:0.170 lr:3.5e-05 updt_s:0.091 data_s:0.006
+Training:  37%|██████████████▌                        | 9360/25000 [16:16<24:53, 10.47step/s]INFO 2026-05-20 22:38:00 ot_train.py:488 step:9K smpl:599K ep:1K epch:2.20 loss:0.008 grdn:0.170 lr:3.5e-05 updt_s:0.090 data_s:0.009
+Training:  38%|██████████████▋                        | 9380/25000 [16:18<26:54,  9.67step/s]INFO 2026-05-20 22:38:02 ot_train.py:488 step:9K smpl:600K ep:1K epch:2.20 loss:0.008 grdn:0.163 lr:3.5e-05 updt_s:0.088 data_s:0.010
+Training:  38%|██████████████▋                        | 9400/25000 [16:20<26:51,  9.68step/s]INFO 2026-05-20 22:38:04 ot_train.py:488 step:9K smpl:602K ep:1K epch:2.21 loss:0.009 grdn:0.172 lr:3.5e-05 updt_s:0.093 data_s:0.007
+Training:  38%|██████████████▋                        | 9420/25000 [16:22<26:51,  9.67step/s]INFO 2026-05-20 22:38:06 ot_train.py:488 step:9K smpl:603K ep:1K epch:2.21 loss:0.008 grdn:0.165 lr:3.5e-05 updt_s:0.091 data_s:0.009
+Training:  38%|██████████████▋                        | 9440/25000 [16:24<24:35, 10.54step/s]INFO 2026-05-20 22:38:08 ot_train.py:488 step:9K smpl:604K ep:1K epch:2.22 loss:0.008 grdn:0.166 lr:3.5e-05 updt_s:0.088 data_s:0.007
+Training:  38%|██████████████▊                        | 9460/25000 [16:26<25:20, 10.22step/s]INFO 2026-05-20 22:38:10 ot_train.py:488 step:9K smpl:605K ep:2K epch:2.22 loss:0.008 grdn:0.169 lr:3.5e-05 updt_s:0.089 data_s:0.010
+Training:  38%|██████████████▊                        | 9480/25000 [16:28<27:13,  9.50step/s]INFO 2026-05-20 22:38:12 ot_train.py:488 step:9K smpl:607K ep:2K epch:2.22 loss:0.008 grdn:0.198 lr:3.4e-05 updt_s:0.089 data_s:0.006
+Training:  38%|██████████████▊                        | 9499/25000 [16:29<24:42, 10.46step/s]INFO 2026-05-20 22:38:14 ot_train.py:488 step:10K smpl:608K ep:2K epch:2.23 loss:0.008 grdn:0.174 lr:3.4e-05 updt_s:0.088 data_s:0.009
+Training:  38%|██████████████▊                        | 9520/25000 [16:32<26:34,  9.71step/s]INFO 2026-05-20 22:38:16 ot_train.py:488 step:10K smpl:609K ep:2K epch:2.23 loss:0.008 grdn:0.177 lr:3.4e-05 updt_s:0.096 data_s:0.006
+Training:  38%|██████████████▉                        | 9540/25000 [16:34<26:44,  9.63step/s]INFO 2026-05-20 22:38:18 ot_train.py:488 step:10K smpl:611K ep:2K epch:2.24 loss:0.008 grdn:0.163 lr:3.4e-05 updt_s:0.087 data_s:0.009
+Training:  38%|██████████████▉                        | 9560/25000 [16:36<25:29, 10.09step/s]INFO 2026-05-20 22:38:20 ot_train.py:488 step:10K smpl:612K ep:2K epch:2.24 loss:0.008 grdn:0.169 lr:3.4e-05 updt_s:0.092 data_s:0.006
+Training:  38%|██████████████▉                        | 9579/25000 [16:38<25:53,  9.92step/s]INFO 2026-05-20 22:38:22 ot_train.py:488 step:10K smpl:613K ep:2K epch:2.25 loss:0.009 grdn:0.194 lr:3.4e-05 updt_s:0.091 data_s:0.009
+Training:  38%|██████████████▉                        | 9599/25000 [16:40<27:02,  9.49step/s]INFO 2026-05-20 22:38:24 ot_train.py:488 step:10K smpl:614K ep:2K epch:2.25 loss:0.009 grdn:0.191 lr:3.4e-05 updt_s:0.089 data_s:0.008
+Training:  38%|███████████████                        | 9620/25000 [16:42<23:36, 10.86step/s]INFO 2026-05-20 22:38:26 ot_train.py:488 step:10K smpl:616K ep:2K epch:2.26 loss:0.009 grdn:0.165 lr:3.4e-05 updt_s:0.086 data_s:0.011
+Training:  39%|███████████████                        | 9639/25000 [16:44<24:24, 10.49step/s]INFO 2026-05-20 22:38:28 ot_train.py:488 step:10K smpl:617K ep:2K epch:2.26 loss:0.008 grdn:0.165 lr:3.4e-05 updt_s:0.096 data_s:0.006
+Training:  39%|███████████████                        | 9660/25000 [16:46<26:47,  9.54step/s]INFO 2026-05-20 22:38:30 ot_train.py:488 step:10K smpl:618K ep:2K epch:2.27 loss:0.008 grdn:0.165 lr:3.4e-05 updt_s:0.089 data_s:0.009
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+Training:  39%|███████████████▏                       | 9699/25000 [16:50<26:35,  9.59step/s]INFO 2026-05-20 22:38:34 ot_train.py:488 step:10K smpl:621K ep:2K epch:2.28 loss:0.008 grdn:0.167 lr:3.4e-05 updt_s:0.095 data_s:0.007
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+Training:  40%|███████████████▌                       | 9960/25000 [17:16<24:34, 10.20step/s]INFO 2026-05-20 22:39:00 ot_train.py:488 step:10K smpl:637K ep:2K epch:2.34 loss:0.008 grdn:0.179 lr:3.3e-05 updt_s:0.089 data_s:0.006
+Training:  40%|███████████████▌                       | 9979/25000 [17:18<24:37, 10.16step/s]INFO 2026-05-20 22:39:02 ot_train.py:488 step:10K smpl:639K ep:2K epch:2.34 loss:0.008 grdn:0.167 lr:3.3e-05 updt_s:0.094 data_s:0.006
+Training:  40%|███████████████▌                       | 9999/25000 [17:19<24:14, 10.31step/s]INFO 2026-05-20 22:39:04 ot_train.py:488 step:10K smpl:640K ep:2K epch:2.35 loss:0.008 grdn:0.158 lr:3.3e-05 updt_s:0.092 data_s:0.008
+INFO 2026-05-20 22:39:04 ot_train.py:502 Checkpoint policy after step 10000
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+Training:  40%|███████████████▍                      | 10119/25000 [17:39<23:48, 10.41step/s]INFO 2026-05-20 22:39:24 ot_train.py:488 step:10K smpl:648K ep:2K epch:2.38 loss:0.009 grdn:0.173 lr:3.3e-05 updt_s:0.091 data_s:0.006
+Training:  41%|███████████████▍                      | 10140/25000 [17:41<26:05,  9.49step/s]INFO 2026-05-20 22:39:26 ot_train.py:488 step:10K smpl:649K ep:2K epch:2.38 loss:0.008 grdn:0.171 lr:3.3e-05 updt_s:0.092 data_s:0.006
+Training:  41%|███████████████▍                      | 10159/25000 [17:43<22:56, 10.78step/s]INFO 2026-05-20 22:39:28 ot_train.py:488 step:10K smpl:650K ep:2K epch:2.38 loss:0.007 grdn:0.156 lr:3.2e-05 updt_s:0.088 data_s:0.008
+Training:  41%|███████████████▍                      | 10180/25000 [17:45<25:51,  9.55step/s]INFO 2026-05-20 22:39:30 ot_train.py:488 step:10K smpl:652K ep:2K epch:2.39 loss:0.008 grdn:0.177 lr:3.2e-05 updt_s:0.089 data_s:0.009
+Training:  41%|███████████████▌                      | 10199/25000 [17:47<23:24, 10.53step/s]INFO 2026-05-20 22:39:31 ot_train.py:488 step:10K smpl:653K ep:2K epch:2.39 loss:0.009 grdn:0.185 lr:3.2e-05 updt_s:0.086 data_s:0.006
+Training:  41%|███████████████▌                      | 10219/25000 [17:49<24:18, 10.14step/s]INFO 2026-05-20 22:39:33 ot_train.py:488 step:10K smpl:654K ep:2K epch:2.40 loss:0.007 grdn:0.170 lr:3.2e-05 updt_s:0.089 data_s:0.008
+Training:  41%|███████████████▌                      | 10240/25000 [17:51<24:15, 10.14step/s]INFO 2026-05-20 22:39:35 ot_train.py:488 step:10K smpl:655K ep:2K epch:2.40 loss:0.008 grdn:0.183 lr:3.2e-05 updt_s:0.088 data_s:0.010
+Training:  41%|███████████████▌                      | 10260/25000 [17:53<24:54,  9.86step/s]INFO 2026-05-20 22:39:37 ot_train.py:488 step:10K smpl:657K ep:2K epch:2.41 loss:0.008 grdn:0.177 lr:3.2e-05 updt_s:0.086 data_s:0.011
+Training:  41%|███████████████▋                      | 10280/25000 [17:55<24:35,  9.98step/s]INFO 2026-05-20 22:39:39 ot_train.py:488 step:10K smpl:658K ep:2K epch:2.41 loss:0.008 grdn:0.173 lr:3.2e-05 updt_s:0.088 data_s:0.009
+Training:  41%|███████████████▋                      | 10299/25000 [17:57<25:27,  9.62step/s]INFO 2026-05-20 22:39:41 ot_train.py:488 step:10K smpl:659K ep:2K epch:2.42 loss:0.007 grdn:0.162 lr:3.2e-05 updt_s:0.091 data_s:0.007
+Training:  41%|███████████████▋                      | 10319/25000 [17:59<23:24, 10.46step/s]INFO 2026-05-20 22:39:43 ot_train.py:488 step:10K smpl:660K ep:2K epch:2.42 loss:0.008 grdn:0.178 lr:3.2e-05 updt_s:0.086 data_s:0.006
+Training:  41%|███████████████▋                      | 10340/25000 [18:01<22:44, 10.74step/s]INFO 2026-05-20 22:39:45 ot_train.py:488 step:10K smpl:662K ep:2K epch:2.43 loss:0.008 grdn:0.182 lr:3.2e-05 updt_s:0.091 data_s:0.006
+Training:  41%|███████████████▋                      | 10359/25000 [18:03<25:11,  9.69step/s]INFO 2026-05-20 22:39:47 ot_train.py:488 step:10K smpl:663K ep:2K epch:2.43 loss:0.008 grdn:0.165 lr:3.2e-05 updt_s:0.094 data_s:0.007
+Training:  42%|███████████████▊                      | 10380/25000 [18:05<24:09, 10.08step/s]INFO 2026-05-20 22:39:49 ot_train.py:488 step:10K smpl:664K ep:2K epch:2.44 loss:0.008 grdn:0.168 lr:3.2e-05 updt_s:0.090 data_s:0.009
+Training:  42%|███████████████▊                      | 10400/25000 [18:07<23:22, 10.41step/s]INFO 2026-05-20 22:39:51 ot_train.py:488 step:10K smpl:666K ep:2K epch:2.44 loss:0.008 grdn:0.180 lr:3.2e-05 updt_s:0.090 data_s:0.006
+Training:  42%|███████████████▊                      | 10420/25000 [18:09<22:42, 10.70step/s]INFO 2026-05-20 22:39:53 ot_train.py:488 step:10K smpl:667K ep:2K epch:2.45 loss:0.009 grdn:0.179 lr:3.2e-05 updt_s:0.088 data_s:0.006
+Training:  42%|███████████████▊                      | 10440/25000 [18:11<25:01,  9.70step/s]INFO 2026-05-20 22:39:55 ot_train.py:488 step:10K smpl:668K ep:2K epch:2.45 loss:0.009 grdn:0.181 lr:3.2e-05 updt_s:0.092 data_s:0.008
+Training:  42%|███████████████▉                      | 10460/25000 [18:13<23:31, 10.30step/s]INFO 2026-05-20 22:39:57 ot_train.py:488 step:10K smpl:669K ep:2K epch:2.45 loss:0.008 grdn:0.178 lr:3.2e-05 updt_s:0.088 data_s:0.008
+Training:  42%|███████████████▉                      | 10480/25000 [18:15<23:49, 10.16step/s]INFO 2026-05-20 22:39:59 ot_train.py:488 step:10K smpl:671K ep:2K epch:2.46 loss:0.009 grdn:0.172 lr:3.1e-05 updt_s:0.085 data_s:0.012
+Training:  42%|███████████████▉                      | 10500/25000 [18:17<23:35, 10.24step/s]INFO 2026-05-20 22:40:01 ot_train.py:488 step:10K smpl:672K ep:2K epch:2.46 loss:0.009 grdn:0.201 lr:3.1e-05 updt_s:0.084 data_s:0.011
+Training:  42%|███████████████▉                      | 10520/25000 [18:19<24:34,  9.82step/s]INFO 2026-05-20 22:40:03 ot_train.py:488 step:11K smpl:673K ep:2K epch:2.47 loss:0.008 grdn:0.180 lr:3.1e-05 updt_s:0.088 data_s:0.009
+Training:  42%|████████████████                      | 10539/25000 [18:20<24:08,  9.98step/s]INFO 2026-05-20 22:40:05 ot_train.py:488 step:11K smpl:675K ep:2K epch:2.47 loss:0.008 grdn:0.171 lr:3.1e-05 updt_s:0.088 data_s:0.009
+Training:  42%|████████████████                      | 10560/25000 [18:23<23:06, 10.41step/s]INFO 2026-05-20 22:40:07 ot_train.py:488 step:11K smpl:676K ep:2K epch:2.48 loss:0.008 grdn:0.178 lr:3.1e-05 updt_s:0.092 data_s:0.006
+Training:  42%|████████████████                      | 10580/25000 [18:25<21:40, 11.09step/s]INFO 2026-05-20 22:40:09 ot_train.py:488 step:11K smpl:677K ep:2K epch:2.48 loss:0.008 grdn:0.175 lr:3.1e-05 updt_s:0.089 data_s:0.008
+Training:  42%|████████████████                      | 10600/25000 [18:27<25:17,  9.49step/s]INFO 2026-05-20 22:40:11 ot_train.py:488 step:11K smpl:678K ep:2K epch:2.49 loss:0.008 grdn:0.173 lr:3.1e-05 updt_s:0.087 data_s:0.012
+Training:  42%|████████████████▏                     | 10619/25000 [18:28<23:05, 10.38step/s]INFO 2026-05-20 22:40:13 ot_train.py:488 step:11K smpl:680K ep:2K epch:2.49 loss:0.009 grdn:0.177 lr:3.1e-05 updt_s:0.089 data_s:0.011
+Training:  43%|████████████████▏                     | 10639/25000 [18:30<24:42,  9.68step/s]INFO 2026-05-20 22:40:15 ot_train.py:488 step:11K smpl:681K ep:2K epch:2.50 loss:0.008 grdn:0.165 lr:3.1e-05 updt_s:0.089 data_s:0.008
+Training:  43%|████████████████▏                     | 10660/25000 [18:33<23:44, 10.07step/s]INFO 2026-05-20 22:40:17 ot_train.py:488 step:11K smpl:682K ep:2K epch:2.50 loss:0.008 grdn:0.173 lr:3.1e-05 updt_s:0.094 data_s:0.006
+Training:  43%|████████████████▏                     | 10680/25000 [18:34<22:51, 10.44step/s]INFO 2026-05-20 22:40:19 ot_train.py:488 step:11K smpl:684K ep:2K epch:2.51 loss:0.007 grdn:0.175 lr:3.1e-05 updt_s:0.085 data_s:0.009
+Training:  43%|████████████████▎                     | 10699/25000 [18:36<24:01,  9.92step/s]INFO 2026-05-20 22:40:21 ot_train.py:488 step:11K smpl:685K ep:2K epch:2.51 loss:0.008 grdn:0.173 lr:3.1e-05 updt_s:0.090 data_s:0.009
+Training:  43%|████████████████▎                     | 10719/25000 [18:38<24:35,  9.68step/s]INFO 2026-05-20 22:40:23 ot_train.py:488 step:11K smpl:686K ep:2K epch:2.52 loss:0.008 grdn:0.167 lr:3.1e-05 updt_s:0.087 data_s:0.011
+Training:  43%|████████████████▎                     | 10739/25000 [18:40<23:01, 10.32step/s]INFO 2026-05-20 22:40:25 ot_train.py:488 step:11K smpl:687K ep:2K epch:2.52 loss:0.008 grdn:0.177 lr:3.1e-05 updt_s:0.090 data_s:0.008
+Training:  43%|████████████████▎                     | 10760/25000 [18:43<25:05,  9.46step/s]INFO 2026-05-20 22:40:27 ot_train.py:488 step:11K smpl:689K ep:2K epch:2.53 loss:0.008 grdn:0.198 lr:3.1e-05 updt_s:0.089 data_s:0.016
+Training:  43%|████████████████▍                     | 10779/25000 [18:44<23:32, 10.07step/s]INFO 2026-05-20 22:40:29 ot_train.py:488 step:11K smpl:690K ep:2K epch:2.53 loss:0.008 grdn:0.179 lr:3.1e-05 updt_s:0.088 data_s:0.008
+Training:  43%|████████████████▍                     | 10800/25000 [18:47<23:06, 10.24step/s]INFO 2026-05-20 22:40:31 ot_train.py:488 step:11K smpl:691K ep:2K epch:2.53 loss:0.009 grdn:0.174 lr:3.1e-05 updt_s:0.091 data_s:0.009
+Training:  43%|████████████████▍                     | 10820/25000 [18:48<21:33, 10.96step/s]INFO 2026-05-20 22:40:33 ot_train.py:488 step:11K smpl:692K ep:2K epch:2.54 loss:0.007 grdn:0.162 lr:3.0e-05 updt_s:0.084 data_s:0.008
+Training:  43%|████████████████▍                     | 10840/25000 [18:50<25:08,  9.39step/s]INFO 2026-05-20 22:40:35 ot_train.py:488 step:11K smpl:694K ep:2K epch:2.54 loss:0.008 grdn:0.176 lr:3.0e-05 updt_s:0.095 data_s:0.008
+Training:  43%|████████████████▌                     | 10859/25000 [18:52<23:05, 10.21step/s]INFO 2026-05-20 22:40:37 ot_train.py:488 step:11K smpl:695K ep:2K epch:2.55 loss:0.008 grdn:0.179 lr:3.0e-05 updt_s:0.084 data_s:0.011
+Training:  44%|████████████████▌                     | 10879/25000 [18:54<22:06, 10.65step/s]INFO 2026-05-20 22:40:39 ot_train.py:488 step:11K smpl:696K ep:2K epch:2.55 loss:0.008 grdn:0.175 lr:3.0e-05 updt_s:0.090 data_s:0.008
+Training:  44%|████████████████▌                     | 10899/25000 [18:56<23:02, 10.20step/s]INFO 2026-05-20 22:40:41 ot_train.py:488 step:11K smpl:698K ep:2K epch:2.56 loss:0.009 grdn:0.191 lr:3.0e-05 updt_s:0.093 data_s:0.007
+Training:  44%|████████████████▌                     | 10919/25000 [18:58<23:48,  9.86step/s]INFO 2026-05-20 22:40:43 ot_train.py:488 step:11K smpl:699K ep:2K epch:2.56 loss:0.009 grdn:0.184 lr:3.0e-05 updt_s:0.090 data_s:0.007
+Training:  44%|████████████████▋                     | 10940/25000 [19:00<23:21, 10.03step/s]INFO 2026-05-20 22:40:45 ot_train.py:488 step:11K smpl:700K ep:2K epch:2.57 loss:0.008 grdn:0.168 lr:3.0e-05 updt_s:0.091 data_s:0.006
+Training:  44%|████████████████▋                     | 10960/25000 [19:02<23:20, 10.03step/s]INFO 2026-05-20 22:40:47 ot_train.py:488 step:11K smpl:701K ep:2K epch:2.57 loss:0.008 grdn:0.169 lr:3.0e-05 updt_s:0.091 data_s:0.008
+Training:  44%|████████████████▋                     | 10980/25000 [19:04<24:06,  9.69step/s]INFO 2026-05-20 22:40:49 ot_train.py:488 step:11K smpl:703K ep:2K epch:2.58 loss:0.009 grdn:0.170 lr:3.0e-05 updt_s:0.092 data_s:0.009
+Training:  44%|████████████████▋                     | 11000/25000 [19:06<23:35,  9.89step/s]INFO 2026-05-20 22:40:51 ot_train.py:488 step:11K smpl:704K ep:2K epch:2.58 loss:0.008 grdn:0.187 lr:3.0e-05 updt_s:0.090 data_s:0.007
+Training:  44%|████████████████▊                     | 11020/25000 [19:08<22:51, 10.19step/s]INFO 2026-05-20 22:40:53 ot_train.py:488 step:11K smpl:705K ep:2K epch:2.59 loss:0.008 grdn:0.170 lr:3.0e-05 updt_s:0.089 data_s:0.009
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+Training:  46%|█████████████████▎                    | 11379/25000 [19:44<21:03, 10.78step/s]INFO 2026-05-20 22:41:28 ot_train.py:488 step:11K smpl:728K ep:2K epch:2.67 loss:0.009 grdn:0.185 lr:2.9e-05 updt_s:0.088 data_s:0.009
+Training:  46%|█████████████████▎                    | 11399/25000 [19:46<22:08, 10.24step/s]INFO 2026-05-20 22:41:30 ot_train.py:488 step:11K smpl:730K ep:2K epch:2.68 loss:0.007 grdn:0.161 lr:2.9e-05 updt_s:0.090 data_s:0.010
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+Training:  46%|█████████████████▍                    | 11460/25000 [19:52<21:26, 10.52step/s]INFO 2026-05-20 22:41:36 ot_train.py:488 step:11K smpl:733K ep:2K epch:2.69 loss:0.008 grdn:0.186 lr:2.8e-05 updt_s:0.086 data_s:0.008
+Training:  46%|█████████████████▍                    | 11480/25000 [19:54<22:45,  9.90step/s]INFO 2026-05-20 22:41:38 ot_train.py:488 step:11K smpl:735K ep:2K epch:2.69 loss:0.009 grdn:0.180 lr:2.8e-05 updt_s:0.089 data_s:0.010
+Training:  46%|█████████████████▍                    | 11500/25000 [19:56<23:23,  9.62step/s]INFO 2026-05-20 22:41:40 ot_train.py:488 step:12K smpl:736K ep:2K epch:2.70 loss:0.009 grdn:0.182 lr:2.8e-05 updt_s:0.090 data_s:0.010
+Training:  46%|█████████████████▌                    | 11520/25000 [19:58<22:31,  9.98step/s]INFO 2026-05-20 22:41:42 ot_train.py:488 step:12K smpl:737K ep:2K epch:2.70 loss:0.007 grdn:0.171 lr:2.8e-05 updt_s:0.090 data_s:0.008
+Training:  46%|█████████████████▌                    | 11539/25000 [20:00<19:43, 11.37step/s]INFO 2026-05-20 22:41:44 ot_train.py:488 step:12K smpl:739K ep:2K epch:2.71 loss:0.008 grdn:0.195 lr:2.8e-05 updt_s:0.089 data_s:0.007
+Training:  46%|█████████████████▌                    | 11560/25000 [20:02<24:32,  9.13step/s]INFO 2026-05-20 22:41:46 ot_train.py:488 step:12K smpl:740K ep:2K epch:2.71 loss:0.008 grdn:0.170 lr:2.8e-05 updt_s:0.098 data_s:0.006
+Training:  46%|█████████████████▌                    | 11579/25000 [20:04<22:59,  9.73step/s]INFO 2026-05-20 22:41:48 ot_train.py:488 step:12K smpl:741K ep:2K epch:2.72 loss:0.008 grdn:0.167 lr:2.8e-05 updt_s:0.087 data_s:0.008
+Training:  46%|█████████████████▋                    | 11599/25000 [20:06<21:44, 10.27step/s]INFO 2026-05-20 22:41:50 ot_train.py:488 step:12K smpl:742K ep:2K epch:2.72 loss:0.007 grdn:0.159 lr:2.8e-05 updt_s:0.091 data_s:0.008
+Training:  46%|█████████████████▋                    | 11619/25000 [20:08<22:26,  9.94step/s]INFO 2026-05-20 22:41:52 ot_train.py:488 step:12K smpl:744K ep:2K epch:2.73 loss:0.009 grdn:0.181 lr:2.8e-05 updt_s:0.087 data_s:0.011
+Training:  47%|█████████████████▋                    | 11639/25000 [20:10<21:08, 10.53step/s]INFO 2026-05-20 22:41:54 ot_train.py:488 step:12K smpl:745K ep:2K epch:2.73 loss:0.008 grdn:0.167 lr:2.8e-05 updt_s:0.086 data_s:0.008
+Training:  47%|█████████████████▋                    | 11660/25000 [20:12<21:15, 10.46step/s]INFO 2026-05-20 22:41:56 ot_train.py:488 step:12K smpl:746K ep:2K epch:2.74 loss:0.008 grdn:0.167 lr:2.8e-05 updt_s:0.091 data_s:0.007
+Training:  47%|█████████████████▊                    | 11680/25000 [20:14<21:54, 10.13step/s]INFO 2026-05-20 22:41:58 ot_train.py:488 step:12K smpl:748K ep:2K epch:2.74 loss:0.007 grdn:0.166 lr:2.8e-05 updt_s:0.089 data_s:0.008
+Training:  47%|█████████████████▊                    | 11699/25000 [20:16<21:15, 10.43step/s]INFO 2026-05-20 22:42:00 ot_train.py:488 step:12K smpl:749K ep:2K epch:2.75 loss:0.009 grdn:0.177 lr:2.8e-05 updt_s:0.090 data_s:0.009
+Training:  47%|█████████████████▊                    | 11720/25000 [20:18<24:41,  8.96step/s]INFO 2026-05-20 22:42:02 ot_train.py:488 step:12K smpl:750K ep:2K epch:2.75 loss:0.008 grdn:0.166 lr:2.8e-05 updt_s:0.091 data_s:0.008
+Training:  47%|█████████████████▊                    | 11740/25000 [20:20<21:20, 10.36step/s]INFO 2026-05-20 22:42:04 ot_train.py:488 step:12K smpl:751K ep:2K epch:2.76 loss:0.008 grdn:0.186 lr:2.8e-05 updt_s:0.087 data_s:0.007
+Training:  47%|█████████████████▉                    | 11760/25000 [20:22<21:18, 10.35step/s]INFO 2026-05-20 22:42:06 ot_train.py:488 step:12K smpl:753K ep:2K epch:2.76 loss:0.008 grdn:0.189 lr:2.8e-05 updt_s:0.089 data_s:0.006
+Training:  47%|█████████████████▉                    | 11779/25000 [20:23<20:24, 10.79step/s]INFO 2026-05-20 22:42:08 ot_train.py:488 step:12K smpl:754K ep:2K epch:2.76 loss:0.007 grdn:0.167 lr:2.7e-05 updt_s:0.088 data_s:0.010
+Training:  47%|█████████████████▉                    | 11800/25000 [20:26<22:20,  9.84step/s]INFO 2026-05-20 22:42:10 ot_train.py:488 step:12K smpl:755K ep:2K epch:2.77 loss:0.008 grdn:0.162 lr:2.7e-05 updt_s:0.091 data_s:0.007
+Training:  47%|█████████████████▉                    | 11819/25000 [20:28<22:10,  9.91step/s]INFO 2026-05-20 22:42:12 ot_train.py:488 step:12K smpl:756K ep:2K epch:2.77 loss:0.007 grdn:0.153 lr:2.7e-05 updt_s:0.088 data_s:0.011
+Training:  47%|█████████████████▉                    | 11839/25000 [20:29<19:36, 11.18step/s]INFO 2026-05-20 22:42:14 ot_train.py:488 step:12K smpl:758K ep:2K epch:2.78 loss:0.008 grdn:0.168 lr:2.7e-05 updt_s:0.082 data_s:0.010
+Training:  47%|██████████████████                    | 11859/25000 [20:32<21:15, 10.30step/s]INFO 2026-05-20 22:42:16 ot_train.py:488 step:12K smpl:759K ep:2K epch:2.78 loss:0.008 grdn:0.170 lr:2.7e-05 updt_s:0.091 data_s:0.009
+Training:  48%|██████████████████                    | 11879/25000 [20:34<22:33,  9.69step/s]INFO 2026-05-20 22:42:18 ot_train.py:488 step:12K smpl:760K ep:2K epch:2.79 loss:0.008 grdn:0.174 lr:2.7e-05 updt_s:0.089 data_s:0.012
+Training:  48%|██████████████████                    | 11899/25000 [20:36<22:47,  9.58step/s]INFO 2026-05-20 22:42:20 ot_train.py:488 step:12K smpl:762K ep:2K epch:2.79 loss:0.008 grdn:0.188 lr:2.7e-05 updt_s:0.090 data_s:0.008
+Training:  48%|██████████████████                    | 11919/25000 [20:38<21:24, 10.18step/s]INFO 2026-05-20 22:42:22 ot_train.py:488 step:12K smpl:763K ep:2K epch:2.80 loss:0.008 grdn:0.174 lr:2.7e-05 updt_s:0.092 data_s:0.008
+Training:  48%|██████████████████▏                   | 11939/25000 [20:40<21:12, 10.27step/s]INFO 2026-05-20 22:42:24 ot_train.py:488 step:12K smpl:764K ep:2K epch:2.80 loss:0.009 grdn:0.189 lr:2.7e-05 updt_s:0.089 data_s:0.007
+Training:  48%|██████████████████▏                   | 11960/25000 [20:42<20:37, 10.54step/s]INFO 2026-05-20 22:42:26 ot_train.py:488 step:12K smpl:765K ep:2K epch:2.81 loss:0.008 grdn:0.182 lr:2.7e-05 updt_s:0.090 data_s:0.008
+Training:  48%|██████████████████▏                   | 11980/25000 [20:44<21:43,  9.99step/s]INFO 2026-05-20 22:42:28 ot_train.py:488 step:12K smpl:767K ep:2K epch:2.81 loss:0.009 grdn:0.184 lr:2.7e-05 updt_s:0.094 data_s:0.008
+Training:  48%|██████████████████▏                   | 12000/25000 [20:46<20:56, 10.35step/s]INFO 2026-05-20 22:42:30 ot_train.py:488 step:12K smpl:768K ep:2K epch:2.82 loss:0.008 grdn:0.165 lr:2.7e-05 updt_s:0.084 data_s:0.012
+INFO 2026-05-20 22:42:30 ot_train.py:502 Checkpoint policy after step 12000
+Training:  48%|██████████████████▎                   | 12020/25000 [20:56<28:47,  7.51step/s]INFO 2026-05-20 22:42:40 ot_train.py:488 step:12K smpl:769K ep:2K epch:2.82 loss:0.009 grdn:0.188 lr:2.7e-05 updt_s:0.069 data_s:0.006
+Training:  48%|██████████████████▎                   | 12040/25000 [20:58<22:02,  9.80step/s]INFO 2026-05-20 22:42:42 ot_train.py:488 step:12K smpl:771K ep:2K epch:2.83 loss:0.008 grdn:0.178 lr:2.7e-05 updt_s:0.095 data_s:0.009
+Training:  48%|██████████████████▎                   | 12060/25000 [21:00<21:13, 10.16step/s]INFO 2026-05-20 22:42:44 ot_train.py:488 step:12K smpl:772K ep:2K epch:2.83 loss:0.008 grdn:0.173 lr:2.7e-05 updt_s:0.085 data_s:0.010
+Training:  48%|██████████████████▎                   | 12080/25000 [21:02<20:22, 10.57step/s]INFO 2026-05-20 22:42:46 ot_train.py:488 step:12K smpl:773K ep:2K epch:2.84 loss:0.009 grdn:0.183 lr:2.7e-05 updt_s:0.084 data_s:0.009
+Training:  48%|██████████████████▍                   | 12099/25000 [21:04<20:00, 10.75step/s]INFO 2026-05-20 22:42:48 ot_train.py:488 step:12K smpl:774K ep:2K epch:2.84 loss:0.008 grdn:0.164 lr:2.6e-05 updt_s:0.087 data_s:0.009
+Training:  48%|██████████████████▍                   | 12120/25000 [21:06<20:54, 10.27step/s]INFO 2026-05-20 22:42:50 ot_train.py:488 step:12K smpl:776K ep:2K epch:2.84 loss:0.007 grdn:0.151 lr:2.6e-05 updt_s:0.088 data_s:0.011
+Training:  49%|██████████████████▍                   | 12140/25000 [21:08<20:14, 10.59step/s]INFO 2026-05-20 22:42:52 ot_train.py:488 step:12K smpl:777K ep:2K epch:2.85 loss:0.008 grdn:0.180 lr:2.6e-05 updt_s:0.087 data_s:0.008
+Training:  49%|██████████████████▍                   | 12160/25000 [21:10<21:32,  9.93step/s]INFO 2026-05-20 22:42:54 ot_train.py:488 step:12K smpl:778K ep:2K epch:2.85 loss:0.008 grdn:0.172 lr:2.6e-05 updt_s:0.088 data_s:0.007
+Training:  49%|██████████████████▌                   | 12180/25000 [21:11<19:32, 10.93step/s]INFO 2026-05-20 22:42:56 ot_train.py:488 step:12K smpl:780K ep:2K epch:2.86 loss:0.008 grdn:0.173 lr:2.6e-05 updt_s:0.086 data_s:0.008
+Training:  49%|██████████████████▌                   | 12200/25000 [21:13<20:49, 10.25step/s]INFO 2026-05-20 22:42:58 ot_train.py:488 step:12K smpl:781K ep:2K epch:2.86 loss:0.007 grdn:0.161 lr:2.6e-05 updt_s:0.089 data_s:0.008
+Training:  49%|██████████████████▌                   | 12220/25000 [21:15<20:29, 10.39step/s]INFO 2026-05-20 22:43:00 ot_train.py:488 step:12K smpl:782K ep:2K epch:2.87 loss:0.008 grdn:0.174 lr:2.6e-05 updt_s:0.086 data_s:0.008
+Training:  49%|██████████████████▌                   | 12240/25000 [21:17<19:15, 11.04step/s]INFO 2026-05-20 22:43:02 ot_train.py:488 step:12K smpl:783K ep:2K epch:2.87 loss:0.008 grdn:0.181 lr:2.6e-05 updt_s:0.086 data_s:0.009
+Training:  49%|██████████████████▋                   | 12260/25000 [21:19<18:59, 11.18step/s]INFO 2026-05-20 22:43:04 ot_train.py:488 step:12K smpl:785K ep:2K epch:2.88 loss:0.008 grdn:0.161 lr:2.6e-05 updt_s:0.088 data_s:0.008
+Training:  49%|██████████████████▋                   | 12280/25000 [21:21<19:57, 10.62step/s]INFO 2026-05-20 22:43:06 ot_train.py:488 step:12K smpl:786K ep:2K epch:2.88 loss:0.009 grdn:0.197 lr:2.6e-05 updt_s:0.090 data_s:0.007
+Training:  49%|██████████████████▋                   | 12299/25000 [21:23<20:50, 10.16step/s]INFO 2026-05-20 22:43:07 ot_train.py:488 step:12K smpl:787K ep:2K epch:2.89 loss:0.008 grdn:0.190 lr:2.6e-05 updt_s:0.082 data_s:0.014
+Training:  49%|██████████████████▋                   | 12319/25000 [21:25<19:12, 11.00step/s]INFO 2026-05-20 22:43:09 ot_train.py:488 step:12K smpl:788K ep:2K epch:2.89 loss:0.008 grdn:0.198 lr:2.6e-05 updt_s:0.088 data_s:0.008
+Training:  49%|██████████████████▊                   | 12339/25000 [21:27<20:44, 10.17step/s]INFO 2026-05-20 22:43:11 ot_train.py:488 step:12K smpl:790K ep:2K epch:2.90 loss:0.008 grdn:0.171 lr:2.6e-05 updt_s:0.089 data_s:0.009
+Training:  49%|██████████████████▊                   | 12359/25000 [21:29<20:55, 10.07step/s]INFO 2026-05-20 22:43:13 ot_train.py:488 step:12K smpl:791K ep:2K epch:2.90 loss:0.008 grdn:0.159 lr:2.6e-05 updt_s:0.090 data_s:0.006
+Training:  50%|██████████████████▊                   | 12379/25000 [21:31<20:47, 10.11step/s]INFO 2026-05-20 22:43:15 ot_train.py:488 step:12K smpl:792K ep:2K epch:2.91 loss:0.008 grdn:0.170 lr:2.6e-05 updt_s:0.088 data_s:0.007
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+Training:  53%|████████████████████                  | 13239/25000 [22:58<18:20, 10.68step/s]INFO 2026-05-20 22:44:42 ot_train.py:488 step:13K smpl:847K ep:2K epch:3.11 loss:0.008 grdn:0.166 lr:2.3e-05 updt_s:0.087 data_s:0.009
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+Training:  53%|████████████████████▏                 | 13279/25000 [23:02<17:23, 11.23step/s]INFO 2026-05-20 22:44:46 ot_train.py:488 step:13K smpl:850K ep:2K epch:3.12 loss:0.008 grdn:0.175 lr:2.3e-05 updt_s:0.084 data_s:0.008
+Training:  53%|████████████████████▏                 | 13299/25000 [23:04<20:55,  9.32step/s]INFO 2026-05-20 22:44:48 ot_train.py:488 step:13K smpl:851K ep:2K epch:3.12 loss:0.008 grdn:0.159 lr:2.3e-05 updt_s:0.091 data_s:0.008
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+Training:  54%|████████████████████▋                 | 13599/25000 [23:33<17:49, 10.66step/s]INFO 2026-05-20 22:45:17 ot_train.py:488 step:14K smpl:870K ep:2K epch:3.19 loss:0.008 grdn:0.182 lr:2.2e-05 updt_s:0.088 data_s:0.006
+Training:  54%|████████████████████▋                 | 13619/25000 [23:35<18:36, 10.19step/s]INFO 2026-05-20 22:45:19 ot_train.py:488 step:14K smpl:872K ep:2K epch:3.20 loss:0.008 grdn:0.187 lr:2.2e-05 updt_s:0.084 data_s:0.010
+Training:  55%|████████████████████▋                 | 13639/25000 [23:37<18:46, 10.09step/s]INFO 2026-05-20 22:45:21 ot_train.py:488 step:14K smpl:873K ep:2K epch:3.20 loss:0.007 grdn:0.165 lr:2.2e-05 updt_s:0.090 data_s:0.007
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+Training:  55%|████████████████████▊                 | 13680/25000 [23:41<19:01,  9.92step/s]INFO 2026-05-20 22:45:25 ot_train.py:488 step:14K smpl:876K ep:2K epch:3.21 loss:0.007 grdn:0.183 lr:2.1e-05 updt_s:0.088 data_s:0.009
+Training:  55%|████████████████████▊                 | 13700/25000 [23:43<18:20, 10.26step/s]INFO 2026-05-20 22:45:27 ot_train.py:488 step:14K smpl:877K ep:2K epch:3.22 loss:0.008 grdn:0.175 lr:2.1e-05 updt_s:0.091 data_s:0.006
+Training:  55%|████████████████████▊                 | 13720/25000 [23:45<18:02, 10.42step/s]INFO 2026-05-20 22:45:29 ot_train.py:488 step:14K smpl:878K ep:2K epch:3.22 loss:0.007 grdn:0.164 lr:2.1e-05 updt_s:0.087 data_s:0.010
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+Training:  55%|████████████████████▉                 | 13779/25000 [23:51<19:07,  9.78step/s]INFO 2026-05-20 22:45:35 ot_train.py:488 step:14K smpl:882K ep:2K epch:3.23 loss:0.008 grdn:0.172 lr:2.1e-05 updt_s:0.090 data_s:0.006
+Training:  55%|████████████████████▉                 | 13800/25000 [23:53<17:53, 10.43step/s]INFO 2026-05-20 22:45:37 ot_train.py:488 step:14K smpl:883K ep:2K epch:3.24 loss:0.008 grdn:0.182 lr:2.1e-05 updt_s:0.088 data_s:0.009
+Training:  55%|█████████████████████                 | 13820/25000 [23:55<18:30, 10.07step/s]INFO 2026-05-20 22:45:39 ot_train.py:488 step:14K smpl:884K ep:2K epch:3.24 loss:0.008 grdn:0.181 lr:2.1e-05 updt_s:0.085 data_s:0.011
+Training:  55%|█████████████████████                 | 13840/25000 [23:56<17:31, 10.62step/s]INFO 2026-05-20 22:45:41 ot_train.py:488 step:14K smpl:886K ep:2K epch:3.25 loss:0.008 grdn:0.195 lr:2.1e-05 updt_s:0.087 data_s:0.006
+Training:  55%|█████████████████████                 | 13860/25000 [23:58<17:42, 10.48step/s]INFO 2026-05-20 22:45:43 ot_train.py:488 step:14K smpl:887K ep:2K epch:3.25 loss:0.008 grdn:0.193 lr:2.1e-05 updt_s:0.088 data_s:0.006
+Training:  56%|█████████████████████                 | 13880/25000 [24:00<18:20, 10.10step/s]INFO 2026-05-20 22:45:45 ot_train.py:488 step:14K smpl:888K ep:2K epch:3.26 loss:0.007 grdn:0.156 lr:2.1e-05 updt_s:0.089 data_s:0.009
+Training:  56%|█████████████████████▏                | 13900/25000 [24:02<18:06, 10.21step/s]INFO 2026-05-20 22:45:47 ot_train.py:488 step:14K smpl:890K ep:2K epch:3.26 loss:0.008 grdn:0.183 lr:2.1e-05 updt_s:0.085 data_s:0.010
+Training:  56%|█████████████████████▏                | 13920/25000 [24:04<16:19, 11.32step/s]INFO 2026-05-20 22:45:48 ot_train.py:488 step:14K smpl:891K ep:2K epch:3.27 loss:0.008 grdn:0.189 lr:2.1e-05 updt_s:0.085 data_s:0.006
+Training:  56%|█████████████████████▏                | 13940/25000 [24:06<18:33,  9.93step/s]INFO 2026-05-20 22:45:51 ot_train.py:488 step:14K smpl:892K ep:2K epch:3.27 loss:0.007 grdn:0.187 lr:2.1e-05 updt_s:0.094 data_s:0.009
+Training:  56%|█████████████████████▏                | 13960/25000 [24:08<17:29, 10.52step/s]INFO 2026-05-20 22:45:52 ot_train.py:488 step:14K smpl:893K ep:2K epch:3.28 loss:0.008 grdn:0.186 lr:2.1e-05 updt_s:0.085 data_s:0.007
+Training:  56%|█████████████████████▏                | 13979/25000 [24:10<17:31, 10.48step/s]INFO 2026-05-20 22:45:54 ot_train.py:488 step:14K smpl:895K ep:2K epch:3.28 loss:0.007 grdn:0.160 lr:2.1e-05 updt_s:0.090 data_s:0.006
+Training:  56%|█████████████████████▎                | 13999/25000 [24:12<17:52, 10.26step/s]INFO 2026-05-20 22:45:56 ot_train.py:488 step:14K smpl:896K ep:2K epch:3.29 loss:0.009 grdn:0.167 lr:2.0e-05 updt_s:0.088 data_s:0.008
+INFO 2026-05-20 22:45:56 ot_train.py:502 Checkpoint policy after step 14000
+Training:  56%|█████████████████████▎                | 14019/25000 [24:22<24:47,  7.38step/s]INFO 2026-05-20 22:46:07 ot_train.py:488 step:14K smpl:897K ep:2K epch:3.29 loss:0.007 grdn:0.162 lr:2.0e-05 updt_s:0.070 data_s:0.006
+Training:  56%|█████████████████████▎                | 14039/25000 [24:24<18:36,  9.82step/s]INFO 2026-05-20 22:46:09 ot_train.py:488 step:14K smpl:899K ep:2K epch:3.29 loss:0.009 grdn:0.168 lr:2.0e-05 updt_s:0.095 data_s:0.010
+Training:  56%|█████████████████████▎                | 14059/25000 [24:26<17:40, 10.32step/s]INFO 2026-05-20 22:46:11 ot_train.py:488 step:14K smpl:900K ep:2K epch:3.30 loss:0.008 grdn:0.173 lr:2.0e-05 updt_s:0.085 data_s:0.008
+Training:  56%|█████████████████████▍                | 14079/25000 [24:28<16:49, 10.82step/s]INFO 2026-05-20 22:46:12 ot_train.py:488 step:14K smpl:901K ep:2K epch:3.30 loss:0.008 grdn:0.180 lr:2.0e-05 updt_s:0.088 data_s:0.006
+Training:  56%|█████████████████████▍                | 14099/25000 [24:30<16:52, 10.77step/s]INFO 2026-05-20 22:46:14 ot_train.py:488 step:14K smpl:902K ep:2K epch:3.31 loss:0.008 grdn:0.189 lr:2.0e-05 updt_s:0.086 data_s:0.010
+Training:  56%|█████████████████████▍                | 14119/25000 [24:32<17:26, 10.40step/s]INFO 2026-05-20 22:46:16 ot_train.py:488 step:14K smpl:904K ep:2K epch:3.31 loss:0.008 grdn:0.167 lr:2.0e-05 updt_s:0.087 data_s:0.009
+Training:  57%|█████████████████████▍                | 14139/25000 [24:34<18:10,  9.96step/s]INFO 2026-05-20 22:46:18 ot_train.py:488 step:14K smpl:905K ep:2K epch:3.32 loss:0.007 grdn:0.178 lr:2.0e-05 updt_s:0.087 data_s:0.008
+Training:  57%|█████████████████████▌                | 14159/25000 [24:36<17:22, 10.40step/s]INFO 2026-05-20 22:46:20 ot_train.py:488 step:14K smpl:906K ep:2K epch:3.32 loss:0.008 grdn:0.168 lr:2.0e-05 updt_s:0.088 data_s:0.006
+Training:  57%|█████████████████████▌                | 14179/25000 [24:38<17:24, 10.36step/s]INFO 2026-05-20 22:46:22 ot_train.py:488 step:14K smpl:908K ep:2K epch:3.33 loss:0.008 grdn:0.170 lr:2.0e-05 updt_s:0.089 data_s:0.006
+Training:  57%|█████████████████████▌                | 14199/25000 [24:40<17:28, 10.30step/s]INFO 2026-05-20 22:46:24 ot_train.py:488 step:14K smpl:909K ep:2K epch:3.33 loss:0.008 grdn:0.153 lr:2.0e-05 updt_s:0.088 data_s:0.008
+Training:  57%|█████████████████████▌                | 14219/25000 [24:42<17:57, 10.01step/s]INFO 2026-05-20 22:46:26 ot_train.py:488 step:14K smpl:910K ep:2K epch:3.34 loss:0.008 grdn:0.173 lr:2.0e-05 updt_s:0.089 data_s:0.007
+Training:  57%|█████████████████████▋                | 14239/25000 [24:44<17:29, 10.25step/s]INFO 2026-05-20 22:46:28 ot_train.py:488 step:14K smpl:911K ep:2K epch:3.34 loss:0.007 grdn:0.163 lr:2.0e-05 updt_s:0.084 data_s:0.009
+Training:  57%|█████████████████████▋                | 14259/25000 [24:46<17:00, 10.53step/s]INFO 2026-05-20 22:46:30 ot_train.py:488 step:14K smpl:913K ep:2K epch:3.35 loss:0.008 grdn:0.173 lr:2.0e-05 updt_s:0.089 data_s:0.008
+Training:  57%|█████████████████████▋                | 14280/25000 [24:48<17:56,  9.96step/s]INFO 2026-05-20 22:46:32 ot_train.py:488 step:14K smpl:914K ep:2K epch:3.35 loss:0.007 grdn:0.156 lr:2.0e-05 updt_s:0.090 data_s:0.010
+Training:  57%|█████████████████████▋                | 14299/25000 [24:49<16:20, 10.92step/s]INFO 2026-05-20 22:46:34 ot_train.py:488 step:14K smpl:915K ep:2K epch:3.36 loss:0.007 grdn:0.171 lr:2.0e-05 updt_s:0.084 data_s:0.010
+Training:  57%|█████████████████████▊                | 14319/25000 [24:51<15:49, 11.25step/s]INFO 2026-05-20 22:46:36 ot_train.py:488 step:14K smpl:916K ep:2K epch:3.36 loss:0.008 grdn:0.186 lr:1.9e-05 updt_s:0.087 data_s:0.008
+Training:  57%|█████████████████████▊                | 14339/25000 [24:53<17:27, 10.18step/s]INFO 2026-05-20 22:46:38 ot_train.py:488 step:14K smpl:918K ep:2K epch:3.37 loss:0.008 grdn:0.169 lr:1.9e-05 updt_s:0.082 data_s:0.013
+Training:  57%|█████████████████████▊                | 14359/25000 [24:55<17:17, 10.25step/s]INFO 2026-05-20 22:46:40 ot_train.py:488 step:14K smpl:919K ep:2K epch:3.37 loss:0.008 grdn:0.182 lr:1.9e-05 updt_s:0.088 data_s:0.006
+Training:  58%|█████████████████████▊                | 14379/25000 [24:57<17:47,  9.95step/s]INFO 2026-05-20 22:46:42 ot_train.py:488 step:14K smpl:920K ep:2K epch:3.37 loss:0.007 grdn:0.171 lr:1.9e-05 updt_s:0.084 data_s:0.010
+Training:  58%|█████████████████████▉                | 14399/25000 [24:59<16:14, 10.88step/s]INFO 2026-05-20 22:46:44 ot_train.py:488 step:14K smpl:922K ep:2K epch:3.38 loss:0.008 grdn:0.188 lr:1.9e-05 updt_s:0.089 data_s:0.009
+Training:  58%|█████████████████████▉                | 14419/25000 [25:01<16:34, 10.64step/s]INFO 2026-05-20 22:46:45 ot_train.py:488 step:14K smpl:923K ep:2K epch:3.38 loss:0.007 grdn:0.173 lr:1.9e-05 updt_s:0.084 data_s:0.010
+Training:  58%|█████████████████████▉                | 14439/25000 [25:03<16:37, 10.58step/s]INFO 2026-05-20 22:46:47 ot_train.py:488 step:14K smpl:924K ep:2K epch:3.39 loss:0.007 grdn:0.162 lr:1.9e-05 updt_s:0.091 data_s:0.008
+Training:  58%|█████████████████████▉                | 14459/25000 [25:05<15:31, 11.31step/s]INFO 2026-05-20 22:46:49 ot_train.py:488 step:14K smpl:925K ep:2K epch:3.39 loss:0.008 grdn:0.176 lr:1.9e-05 updt_s:0.084 data_s:0.006
+Training:  58%|██████████████████████                | 14479/25000 [25:07<16:30, 10.62step/s]INFO 2026-05-20 22:46:51 ot_train.py:488 step:14K smpl:927K ep:2K epch:3.40 loss:0.008 grdn:0.172 lr:1.9e-05 updt_s:0.089 data_s:0.007
+Training:  58%|██████████████████████                | 14499/25000 [25:09<16:43, 10.47step/s]INFO 2026-05-20 22:46:53 ot_train.py:488 step:14K smpl:928K ep:2K epch:3.40 loss:0.008 grdn:0.181 lr:1.9e-05 updt_s:0.088 data_s:0.008
+Training:  58%|██████████████████████                | 14520/25000 [25:11<16:40, 10.47step/s]INFO 2026-05-20 22:46:55 ot_train.py:488 step:15K smpl:929K ep:2K epch:3.41 loss:0.009 grdn:0.184 lr:1.9e-05 updt_s:0.087 data_s:0.008
+Training:  58%|██████████████████████                | 14540/25000 [25:13<15:28, 11.26step/s]INFO 2026-05-20 22:46:57 ot_train.py:488 step:15K smpl:931K ep:2K epch:3.41 loss:0.007 grdn:0.165 lr:1.9e-05 updt_s:0.086 data_s:0.006
+Training:  58%|██████████████████████▏               | 14560/25000 [25:15<18:04,  9.62step/s]INFO 2026-05-20 22:46:59 ot_train.py:488 step:15K smpl:932K ep:2K epch:3.42 loss:0.007 grdn:0.159 lr:1.9e-05 updt_s:0.092 data_s:0.006
+Training:  58%|██████████████████████▏               | 14579/25000 [25:16<16:38, 10.44step/s]INFO 2026-05-20 22:47:01 ot_train.py:488 step:15K smpl:933K ep:2K epch:3.42 loss:0.007 grdn:0.160 lr:1.9e-05 updt_s:0.081 data_s:0.009
+Training:  58%|██████████████████████▏               | 14599/25000 [25:18<16:39, 10.41step/s]INFO 2026-05-20 22:47:03 ot_train.py:488 step:15K smpl:934K ep:2K epch:3.43 loss:0.008 grdn:0.170 lr:1.9e-05 updt_s:0.088 data_s:0.009
+Training:  58%|██████████████████████▏               | 14619/25000 [25:20<15:53, 10.89step/s]INFO 2026-05-20 22:47:05 ot_train.py:488 step:15K smpl:936K ep:2K epch:3.43 loss:0.008 grdn:0.165 lr:1.9e-05 updt_s:0.086 data_s:0.008
+Training:  59%|██████████████████████▎               | 14639/25000 [25:22<16:18, 10.59step/s]INFO 2026-05-20 22:47:07 ot_train.py:488 step:15K smpl:937K ep:2K epch:3.44 loss:0.008 grdn:0.170 lr:1.9e-05 updt_s:0.084 data_s:0.009
+Training:  59%|██████████████████████▎               | 14659/25000 [25:24<17:08, 10.05step/s]INFO 2026-05-20 22:47:09 ot_train.py:488 step:15K smpl:938K ep:2K epch:3.44 loss:0.008 grdn:0.177 lr:1.8e-05 updt_s:0.085 data_s:0.010
+Training:  59%|██████████████████████▎               | 14679/25000 [25:26<17:54,  9.60step/s]INFO 2026-05-20 22:47:11 ot_train.py:488 step:15K smpl:940K ep:2K epch:3.45 loss:0.008 grdn:0.173 lr:1.8e-05 updt_s:0.087 data_s:0.009
+Training:  59%|██████████████████████▎               | 14699/25000 [25:28<17:01, 10.09step/s]INFO 2026-05-20 22:47:12 ot_train.py:488 step:15K smpl:941K ep:2K epch:3.45 loss:0.008 grdn:0.169 lr:1.8e-05 updt_s:0.082 data_s:0.010
+Training:  59%|██████████████████████▎               | 14719/25000 [25:30<15:33, 11.01step/s]INFO 2026-05-20 22:47:14 ot_train.py:488 step:15K smpl:942K ep:2K epch:3.45 loss:0.009 grdn:0.181 lr:1.8e-05 updt_s:0.085 data_s:0.009
+Training:  59%|██████████████████████▍               | 14739/25000 [25:32<16:35, 10.31step/s]INFO 2026-05-20 22:47:16 ot_train.py:488 step:15K smpl:943K ep:2K epch:3.46 loss:0.007 grdn:0.155 lr:1.8e-05 updt_s:0.090 data_s:0.006
+Training:  59%|██████████████████████▍               | 14759/25000 [25:34<16:40, 10.23step/s]INFO 2026-05-20 22:47:18 ot_train.py:488 step:15K smpl:945K ep:2K epch:3.46 loss:0.007 grdn:0.160 lr:1.8e-05 updt_s:0.085 data_s:0.010
+Training:  59%|██████████████████████▍               | 14779/25000 [25:36<16:09, 10.55step/s]INFO 2026-05-20 22:47:20 ot_train.py:488 step:15K smpl:946K ep:2K epch:3.47 loss:0.007 grdn:0.172 lr:1.8e-05 updt_s:0.082 data_s:0.010
+Training:  59%|██████████████████████▍               | 14799/25000 [25:38<15:11, 11.19step/s]INFO 2026-05-20 22:47:22 ot_train.py:488 step:15K smpl:947K ep:2K epch:3.47 loss:0.007 grdn:0.163 lr:1.8e-05 updt_s:0.087 data_s:0.011
+Training:  59%|██████████████████████▌               | 14819/25000 [25:40<16:31, 10.27step/s]INFO 2026-05-20 22:47:24 ot_train.py:488 step:15K smpl:948K ep:2K epch:3.48 loss:0.008 grdn:0.173 lr:1.8e-05 updt_s:0.086 data_s:0.006
+Training:  59%|██████████████████████▌               | 14839/25000 [25:41<16:05, 10.52step/s]INFO 2026-05-20 22:47:26 ot_train.py:488 step:15K smpl:950K ep:2K epch:3.48 loss:0.007 grdn:0.180 lr:1.8e-05 updt_s:0.085 data_s:0.007
+Training:  59%|██████████████████████▌               | 14860/25000 [25:44<17:01,  9.92step/s]INFO 2026-05-20 22:47:28 ot_train.py:488 step:15K smpl:951K ep:2K epch:3.49 loss:0.008 grdn:0.179 lr:1.8e-05 updt_s:0.093 data_s:0.006
+Training:  60%|██████████████████████▌               | 14880/25000 [25:45<16:14, 10.39step/s]INFO 2026-05-20 22:47:30 ot_train.py:488 step:15K smpl:952K ep:2K epch:3.49 loss:0.007 grdn:0.170 lr:1.8e-05 updt_s:0.085 data_s:0.007
+Training:  60%|██████████████████████▋               | 14900/25000 [25:47<16:26, 10.24step/s]INFO 2026-05-20 22:47:32 ot_train.py:488 step:15K smpl:954K ep:2K epch:3.50 loss:0.007 grdn:0.181 lr:1.8e-05 updt_s:0.084 data_s:0.012
+Training:  60%|██████████████████████▋               | 14920/25000 [25:49<15:39, 10.72step/s]INFO 2026-05-20 22:47:34 ot_train.py:488 step:15K smpl:955K ep:2K epch:3.50 loss:0.007 grdn:0.170 lr:1.8e-05 updt_s:0.086 data_s:0.006
+Training:  60%|██████████████████████▋               | 14940/25000 [25:51<16:56,  9.90step/s]INFO 2026-05-20 22:47:36 ot_train.py:488 step:15K smpl:956K ep:2K epch:3.51 loss:0.008 grdn:0.175 lr:1.8e-05 updt_s:0.085 data_s:0.012
+Training:  60%|██████████████████████▋               | 14959/25000 [25:53<15:25, 10.85step/s]INFO 2026-05-20 22:47:38 ot_train.py:488 step:15K smpl:957K ep:2K epch:3.51 loss:0.008 grdn:0.184 lr:1.8e-05 updt_s:0.088 data_s:0.010
+Training:  60%|██████████████████████▊               | 14979/25000 [25:55<16:08, 10.34step/s]INFO 2026-05-20 22:47:39 ot_train.py:488 step:15K smpl:959K ep:2K epch:3.52 loss:0.007 grdn:0.187 lr:1.7e-05 updt_s:0.080 data_s:0.012
+Training:  60%|██████████████████████▊               | 14999/25000 [25:57<17:18,  9.63step/s]INFO 2026-05-20 22:47:41 ot_train.py:488 step:15K smpl:960K ep:2K epch:3.52 loss:0.008 grdn:0.162 lr:1.7e-05 updt_s:0.091 data_s:0.006
+Training:  60%|██████████████████████▊               | 15020/25000 [25:59<16:19, 10.18step/s]INFO 2026-05-20 22:47:43 ot_train.py:488 step:15K smpl:961K ep:2K epch:3.52 loss:0.007 grdn:0.148 lr:1.7e-05 updt_s:0.089 data_s:0.007
+Training:  60%|██████████████████████▊               | 15040/25000 [26:01<16:17, 10.19step/s]INFO 2026-05-20 22:47:45 ot_train.py:488 step:15K smpl:963K ep:2K epch:3.53 loss:0.007 grdn:0.149 lr:1.7e-05 updt_s:0.087 data_s:0.009
+Training:  60%|██████████████████████▉               | 15060/25000 [26:03<15:48, 10.48step/s]INFO 2026-05-20 22:47:47 ot_train.py:488 step:15K smpl:964K ep:2K epch:3.53 loss:0.007 grdn:0.162 lr:1.7e-05 updt_s:0.089 data_s:0.006
+Training:  60%|██████████████████████▉               | 15080/25000 [26:05<15:59, 10.34step/s]INFO 2026-05-20 22:47:49 ot_train.py:488 step:15K smpl:965K ep:2K epch:3.54 loss:0.008 grdn:0.181 lr:1.7e-05 updt_s:0.091 data_s:0.006
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+Training:  61%|███████████████████████               | 15139/25000 [26:11<15:29, 10.61step/s]INFO 2026-05-20 22:47:55 ot_train.py:488 step:15K smpl:969K ep:2K epch:3.55 loss:0.007 grdn:0.153 lr:1.7e-05 updt_s:0.089 data_s:0.009
+Training:  61%|███████████████████████               | 15159/25000 [26:13<16:50,  9.74step/s]INFO 2026-05-20 22:47:57 ot_train.py:488 step:15K smpl:970K ep:2K epch:3.56 loss:0.008 grdn:0.175 lr:1.7e-05 updt_s:0.087 data_s:0.008
+Training:  61%|███████████████████████               | 15179/25000 [26:14<14:43, 11.12step/s]INFO 2026-05-20 22:47:59 ot_train.py:488 step:15K smpl:972K ep:2K epch:3.56 loss:0.007 grdn:0.164 lr:1.7e-05 updt_s:0.086 data_s:0.007
+Training:  61%|███████████████████████               | 15199/25000 [26:16<16:18, 10.01step/s]INFO 2026-05-20 22:48:01 ot_train.py:488 step:15K smpl:973K ep:2K epch:3.57 loss:0.008 grdn:0.194 lr:1.7e-05 updt_s:0.088 data_s:0.009
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+Training:  62%|███████████████████████▍              | 15399/25000 [26:36<15:20, 10.43step/s]INFO 2026-05-20 22:48:20 ot_train.py:488 step:15K smpl:986K ep:2K epch:3.61 loss:0.007 grdn:0.171 lr:1.6e-05 updt_s:0.087 data_s:0.010
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+Training:  62%|███████████████████████▍              | 15440/25000 [26:40<15:15, 10.44step/s]INFO 2026-05-20 22:48:24 ot_train.py:488 step:15K smpl:988K ep:2K epch:3.62 loss:0.008 grdn:0.181 lr:1.6e-05 updt_s:0.089 data_s:0.006
+Training:  62%|███████████████████████▍              | 15460/25000 [26:42<15:29, 10.27step/s]INFO 2026-05-20 22:48:26 ot_train.py:488 step:15K smpl:989K ep:2K epch:3.63 loss:0.007 grdn:0.169 lr:1.6e-05 updt_s:0.086 data_s:0.009
+Training:  62%|███████████████████████▌              | 15479/25000 [26:44<16:08,  9.83step/s]INFO 2026-05-20 22:48:28 ot_train.py:488 step:15K smpl:991K ep:2K epch:3.63 loss:0.008 grdn:0.175 lr:1.6e-05 updt_s:0.091 data_s:0.008
+Training:  62%|███████████████████████▌              | 15499/25000 [26:46<15:37, 10.13step/s]INFO 2026-05-20 22:48:30 ot_train.py:488 step:16K smpl:992K ep:2K epch:3.64 loss:0.008 grdn:0.193 lr:1.6e-05 updt_s:0.088 data_s:0.006
+Training:  62%|███████████████████████▌              | 15520/25000 [26:48<15:13, 10.37step/s]INFO 2026-05-20 22:48:32 ot_train.py:488 step:16K smpl:993K ep:2K epch:3.64 loss:0.008 grdn:0.189 lr:1.6e-05 updt_s:0.088 data_s:0.012
+Training:  62%|███████████████████████▌              | 15540/25000 [26:50<15:07, 10.43step/s]INFO 2026-05-20 22:48:34 ot_train.py:488 step:16K smpl:995K ep:2K epch:3.65 loss:0.007 grdn:0.187 lr:1.6e-05 updt_s:0.087 data_s:0.009
+Training:  62%|███████████████████████▋              | 15560/25000 [26:52<14:52, 10.58step/s]INFO 2026-05-20 22:48:36 ot_train.py:488 step:16K smpl:996K ep:2K epch:3.65 loss:0.008 grdn:0.189 lr:1.6e-05 updt_s:0.091 data_s:0.006
+Training:  62%|███████████████████████▋              | 15580/25000 [26:54<15:09, 10.35step/s]INFO 2026-05-20 22:48:38 ot_train.py:488 step:16K smpl:997K ep:2K epch:3.66 loss:0.007 grdn:0.162 lr:1.6e-05 updt_s:0.086 data_s:0.008
+Training:  62%|███████████████████████▋              | 15600/25000 [26:56<15:16, 10.26step/s]INFO 2026-05-20 22:48:40 ot_train.py:488 step:16K smpl:998K ep:2K epch:3.66 loss:0.008 grdn:0.191 lr:1.6e-05 updt_s:0.088 data_s:0.009
+Training:  62%|███████████████████████▋              | 15620/25000 [26:58<15:58,  9.79step/s]INFO 2026-05-20 22:48:42 ot_train.py:488 step:16K smpl:1000K ep:2K epch:3.67 loss:0.008 grdn:0.166 lr:1.6e-05 updt_s:0.091 data_s:0.006
+Training:  63%|███████████████████████▊              | 15640/25000 [27:00<15:12, 10.26step/s]INFO 2026-05-20 22:48:44 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.67 loss:0.007 grdn:0.166 lr:1.6e-05 updt_s:0.087 data_s:0.010
+Training:  63%|███████████████████████▊              | 15659/25000 [27:01<15:19, 10.16step/s]INFO 2026-05-20 22:48:46 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.68 loss:0.008 grdn:0.201 lr:1.5e-05 updt_s:0.089 data_s:0.008
+Training:  63%|███████████████████████▊              | 15680/25000 [27:04<15:08, 10.26step/s]INFO 2026-05-20 22:48:48 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.68 loss:0.008 grdn:0.179 lr:1.5e-05 updt_s:0.092 data_s:0.010
+Training:  63%|███████████████████████▊              | 15700/25000 [27:05<14:26, 10.74step/s]INFO 2026-05-20 22:48:50 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.68 loss:0.008 grdn:0.178 lr:1.5e-05 updt_s:0.088 data_s:0.006
+Training:  63%|███████████████████████▉              | 15720/25000 [27:08<15:09, 10.20step/s]INFO 2026-05-20 22:48:52 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.69 loss:0.008 grdn:0.173 lr:1.5e-05 updt_s:0.089 data_s:0.009
+Training:  63%|███████████████████████▉              | 15740/25000 [27:09<14:05, 10.96step/s]INFO 2026-05-20 22:48:54 ot_train.py:488 step:16K smpl:1M ep:2K epch:3.69 loss:0.007 grdn:0.157 lr:1.5e-05 updt_s:0.089 data_s:0.007
+Training:  63%|███████████████████████▉              | 15760/25000 [27:11<14:49, 10.39step/s]INFO 2026-05-20 22:48:56 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.70 loss:0.008 grdn:0.172 lr:1.5e-05 updt_s:0.088 data_s:0.010
+Training:  63%|███████████████████████▉              | 15780/25000 [27:13<14:59, 10.25step/s]INFO 2026-05-20 22:48:58 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.70 loss:0.007 grdn:0.175 lr:1.5e-05 updt_s:0.089 data_s:0.008
+Training:  63%|████████████████████████              | 15800/25000 [27:15<14:48, 10.35step/s]INFO 2026-05-20 22:49:00 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.71 loss:0.008 grdn:0.167 lr:1.5e-05 updt_s:0.091 data_s:0.008
+Training:  63%|████████████████████████              | 15820/25000 [27:17<14:24, 10.62step/s]INFO 2026-05-20 22:49:02 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.71 loss:0.008 grdn:0.176 lr:1.5e-05 updt_s:0.088 data_s:0.008
+Training:  63%|████████████████████████              | 15840/25000 [27:19<14:40, 10.41step/s]INFO 2026-05-20 22:49:04 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.72 loss:0.008 grdn:0.188 lr:1.5e-05 updt_s:0.090 data_s:0.006
+Training:  63%|████████████████████████              | 15860/25000 [27:21<13:44, 11.09step/s]INFO 2026-05-20 22:49:05 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.72 loss:0.007 grdn:0.177 lr:1.5e-05 updt_s:0.085 data_s:0.006
+Training:  64%|████████████████████████▏             | 15880/25000 [27:23<14:41, 10.34step/s]INFO 2026-05-20 22:49:07 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.73 loss:0.008 grdn:0.198 lr:1.5e-05 updt_s:0.093 data_s:0.006
+Training:  64%|████████████████████████▏             | 15900/25000 [27:25<14:54, 10.17step/s]INFO 2026-05-20 22:49:09 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.73 loss:0.008 grdn:0.197 lr:1.5e-05 updt_s:0.090 data_s:0.006
+Training:  64%|████████████████████████▏             | 15920/25000 [27:27<15:17,  9.90step/s]INFO 2026-05-20 22:49:11 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.74 loss:0.007 grdn:0.178 lr:1.5e-05 updt_s:0.085 data_s:0.009
+Training:  64%|████████████████████████▏             | 15939/25000 [27:29<14:27, 10.45step/s]INFO 2026-05-20 22:49:13 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.74 loss:0.007 grdn:0.169 lr:1.5e-05 updt_s:0.087 data_s:0.009
+Training:  64%|████████████████████████▎             | 15959/25000 [27:31<15:16,  9.87step/s]INFO 2026-05-20 22:49:15 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.75 loss:0.009 grdn:0.182 lr:1.5e-05 updt_s:0.090 data_s:0.009
+Training:  64%|████████████████████████▎             | 15979/25000 [27:33<14:44, 10.20step/s]INFO 2026-05-20 22:49:17 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.75 loss:0.008 grdn:0.171 lr:1.5e-05 updt_s:0.087 data_s:0.009
+Training:  64%|████████████████████████▎             | 15999/25000 [27:35<13:05, 11.45step/s]INFO 2026-05-20 22:49:19 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.75 loss:0.007 grdn:0.161 lr:1.4e-05 updt_s:0.086 data_s:0.006
+INFO 2026-05-20 22:49:19 ot_train.py:502 Checkpoint policy after step 16000
+Training:  64%|████████████████████████▎             | 16019/25000 [27:45<21:24,  6.99step/s]INFO 2026-05-20 22:49:30 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.76 loss:0.008 grdn:0.174 lr:1.4e-05 updt_s:0.072 data_s:0.006
+Training:  64%|████████████████████████▍             | 16039/25000 [27:48<15:05,  9.90step/s]INFO 2026-05-20 22:49:32 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.76 loss:0.008 grdn:0.171 lr:1.4e-05 updt_s:0.092 data_s:0.008
+Training:  64%|████████████████████████▍             | 16059/25000 [27:49<14:06, 10.56step/s]INFO 2026-05-20 22:49:34 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.77 loss:0.007 grdn:0.168 lr:1.4e-05 updt_s:0.087 data_s:0.010
+Training:  64%|████████████████████████▍             | 16079/25000 [27:51<14:18, 10.39step/s]INFO 2026-05-20 22:49:36 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.77 loss:0.008 grdn:0.165 lr:1.4e-05 updt_s:0.087 data_s:0.007
+Training:  64%|████████████████████████▍             | 16099/25000 [27:53<14:27, 10.26step/s]INFO 2026-05-20 22:49:38 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.78 loss:0.008 grdn:0.167 lr:1.4e-05 updt_s:0.088 data_s:0.006
+Training:  64%|████████████████████████▌             | 16119/25000 [27:55<14:41, 10.08step/s]INFO 2026-05-20 22:49:40 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.78 loss:0.007 grdn:0.165 lr:1.4e-05 updt_s:0.089 data_s:0.006
+Training:  65%|████████████████████████▌             | 16139/25000 [27:57<14:37, 10.10step/s]INFO 2026-05-20 22:49:42 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.79 loss:0.008 grdn:0.164 lr:1.4e-05 updt_s:0.086 data_s:0.006
+Training:  65%|████████████████████████▌             | 16160/25000 [27:59<14:31, 10.15step/s]INFO 2026-05-20 22:49:43 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.79 loss:0.007 grdn:0.159 lr:1.4e-05 updt_s:0.088 data_s:0.008
+Training:  65%|████████████████████████▌             | 16180/25000 [28:01<14:22, 10.23step/s]INFO 2026-05-20 22:49:45 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.80 loss:0.007 grdn:0.170 lr:1.4e-05 updt_s:0.091 data_s:0.008
+Training:  65%|████████████████████████▌             | 16200/25000 [28:03<14:34, 10.06step/s]INFO 2026-05-20 22:49:47 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.80 loss:0.007 grdn:0.162 lr:1.4e-05 updt_s:0.085 data_s:0.012
+Training:  65%|████████████████████████▋             | 16219/25000 [28:05<15:23,  9.51step/s]INFO 2026-05-20 22:49:49 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.81 loss:0.008 grdn:0.185 lr:1.4e-05 updt_s:0.091 data_s:0.007
+Training:  65%|████████████████████████▋             | 16239/25000 [28:07<14:19, 10.19step/s]INFO 2026-05-20 22:49:51 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.81 loss:0.008 grdn:0.164 lr:1.4e-05 updt_s:0.088 data_s:0.007
+Training:  65%|████████████████████████▋             | 16260/25000 [28:09<14:10, 10.28step/s]INFO 2026-05-20 22:49:53 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.82 loss:0.008 grdn:0.181 lr:1.4e-05 updt_s:0.088 data_s:0.010
+Training:  65%|████████████████████████▋             | 16280/25000 [28:11<12:55, 11.25step/s]INFO 2026-05-20 22:49:55 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.82 loss:0.008 grdn:0.179 lr:1.4e-05 updt_s:0.083 data_s:0.009
+Training:  65%|████████████████████████▊             | 16300/25000 [28:13<13:26, 10.79step/s]INFO 2026-05-20 22:49:57 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.83 loss:0.008 grdn:0.183 lr:1.4e-05 updt_s:0.090 data_s:0.010
+Training:  65%|████████████████████████▊             | 16320/25000 [28:15<14:24, 10.04step/s]INFO 2026-05-20 22:49:59 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.83 loss:0.008 grdn:0.176 lr:1.4e-05 updt_s:0.090 data_s:0.006
+Training:  65%|████████████████████████▊             | 16339/25000 [28:17<13:34, 10.63step/s]INFO 2026-05-20 22:50:01 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.83 loss:0.007 grdn:0.158 lr:1.4e-05 updt_s:0.090 data_s:0.007
+Training:  65%|████████████████████████▊             | 16359/25000 [28:19<13:36, 10.59step/s]INFO 2026-05-20 22:50:03 ot_train.py:488 step:16K smpl:1M ep:3K epch:3.84 loss:0.008 grdn:0.187 lr:1.3e-05 updt_s:0.088 data_s:0.009
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+Training:  72%|███████████████████████████▎          | 17999/25000 [31:01<10:45, 10.84step/s]INFO 2026-05-20 22:52:46 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.22 loss:0.008 grdn:0.177 lr:9.2e-06 updt_s:0.087 data_s:0.008
+INFO 2026-05-20 22:52:46 ot_train.py:502 Checkpoint policy after step 18000
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+Training:  73%|███████████████████████████▌          | 18160/25000 [31:26<11:35,  9.83step/s]INFO 2026-05-20 22:53:10 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.26 loss:0.007 grdn:0.155 lr:8.8e-06 updt_s:0.090 data_s:0.007
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+Training:  73%|███████████████████████████▋          | 18220/25000 [31:32<10:51, 10.41step/s]INFO 2026-05-20 22:53:16 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.28 loss:0.007 grdn:0.168 lr:8.6e-06 updt_s:0.089 data_s:0.007
+Training:  73%|███████████████████████████▋          | 18240/25000 [31:34<11:34,  9.73step/s]INFO 2026-05-20 22:53:18 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.28 loss:0.008 grdn:0.187 lr:8.6e-06 updt_s:0.090 data_s:0.008
+Training:  73%|███████████████████████████▊          | 18260/25000 [31:36<10:18, 10.91step/s]INFO 2026-05-20 22:53:20 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.29 loss:0.008 grdn:0.171 lr:8.5e-06 updt_s:0.086 data_s:0.008
+Training:  73%|███████████████████████████▊          | 18279/25000 [31:37<11:11, 10.00step/s]INFO 2026-05-20 22:53:22 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.29 loss:0.009 grdn:0.192 lr:8.5e-06 updt_s:0.088 data_s:0.013
+Training:  73%|███████████████████████████▊          | 18300/25000 [31:40<10:58, 10.17step/s]INFO 2026-05-20 22:53:24 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.29 loss:0.008 grdn:0.178 lr:8.4e-06 updt_s:0.088 data_s:0.008
+Training:  73%|███████████████████████████▊          | 18319/25000 [31:41<10:59, 10.14step/s]INFO 2026-05-20 22:53:26 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.30 loss:0.007 grdn:0.179 lr:8.4e-06 updt_s:0.087 data_s:0.010
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+Training:  73%|███████████████████████████▉          | 18359/25000 [31:45<11:06,  9.96step/s]INFO 2026-05-20 22:53:30 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.31 loss:0.008 grdn:0.172 lr:8.3e-06 updt_s:0.084 data_s:0.011
+Training:  74%|███████████████████████████▉          | 18379/25000 [31:47<10:25, 10.59step/s]INFO 2026-05-20 22:53:32 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.31 loss:0.008 grdn:0.186 lr:8.2e-06 updt_s:0.089 data_s:0.006
+Training:  74%|███████████████████████████▉          | 18399/25000 [31:49<10:48, 10.18step/s]INFO 2026-05-20 22:53:34 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.32 loss:0.007 grdn:0.166 lr:8.2e-06 updt_s:0.093 data_s:0.006
+Training:  74%|███████████████████████████▉          | 18419/25000 [31:51<10:37, 10.33step/s]INFO 2026-05-20 22:53:36 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.32 loss:0.007 grdn:0.166 lr:8.2e-06 updt_s:0.086 data_s:0.009
+Training:  74%|████████████████████████████          | 18439/25000 [31:53<11:00,  9.94step/s]INFO 2026-05-20 22:53:38 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.33 loss:0.008 grdn:0.167 lr:8.1e-06 updt_s:0.089 data_s:0.008
+Training:  74%|████████████████████████████          | 18459/25000 [31:55<10:16, 10.60step/s]INFO 2026-05-20 22:53:39 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.33 loss:0.008 grdn:0.168 lr:8.1e-06 updt_s:0.088 data_s:0.008
+Training:  74%|████████████████████████████          | 18479/25000 [31:57<11:08,  9.75step/s]INFO 2026-05-20 22:53:41 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.34 loss:0.007 grdn:0.174 lr:8.0e-06 updt_s:0.091 data_s:0.008
+Training:  74%|████████████████████████████          | 18499/25000 [31:59<11:14,  9.64step/s]INFO 2026-05-20 22:53:43 ot_train.py:488 step:18K smpl:1M ep:3K epch:4.34 loss:0.007 grdn:0.177 lr:8.0e-06 updt_s:0.089 data_s:0.008
+Training:  74%|████████████████████████████▏         | 18519/25000 [32:01<09:40, 11.17step/s]INFO 2026-05-20 22:53:45 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.35 loss:0.008 grdn:0.182 lr:7.9e-06 updt_s:0.083 data_s:0.007
+Training:  74%|████████████████████████████▏         | 18539/25000 [32:03<09:48, 10.98step/s]INFO 2026-05-20 22:53:47 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.35 loss:0.008 grdn:0.185 lr:7.9e-06 updt_s:0.087 data_s:0.008
+Training:  74%|████████████████████████████▏         | 18559/25000 [32:05<11:02,  9.72step/s]INFO 2026-05-20 22:53:49 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.36 loss:0.007 grdn:0.171 lr:7.8e-06 updt_s:0.090 data_s:0.006
+Training:  74%|████████████████████████████▏         | 18579/25000 [32:07<10:16, 10.42step/s]INFO 2026-05-20 22:53:51 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.36 loss:0.007 grdn:0.182 lr:7.8e-06 updt_s:0.089 data_s:0.006
+Training:  74%|████████████████████████████▎         | 18599/25000 [32:09<10:53,  9.80step/s]INFO 2026-05-20 22:53:53 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.37 loss:0.008 grdn:0.185 lr:7.7e-06 updt_s:0.092 data_s:0.006
+Training:  74%|████████████████████████████▎         | 18619/25000 [32:11<09:39, 11.01step/s]INFO 2026-05-20 22:53:55 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.37 loss:0.008 grdn:0.185 lr:7.7e-06 updt_s:0.088 data_s:0.010
+Training:  75%|████████████████████████████▎         | 18639/25000 [32:13<10:24, 10.19step/s]INFO 2026-05-20 22:53:57 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.37 loss:0.007 grdn:0.173 lr:7.6e-06 updt_s:0.085 data_s:0.006
+Training:  75%|████████████████████████████▎         | 18659/25000 [32:15<09:56, 10.63step/s]INFO 2026-05-20 22:53:59 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.38 loss:0.007 grdn:0.170 lr:7.6e-06 updt_s:0.091 data_s:0.011
+Training:  75%|████████████████████████████▍         | 18679/25000 [32:17<10:45,  9.79step/s]INFO 2026-05-20 22:54:01 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.38 loss:0.008 grdn:0.180 lr:7.6e-06 updt_s:0.089 data_s:0.008
+Training:  75%|████████████████████████████▍         | 18699/25000 [32:18<09:57, 10.55step/s]INFO 2026-05-20 22:54:03 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.39 loss:0.008 grdn:0.182 lr:7.5e-06 updt_s:0.086 data_s:0.008
+Training:  75%|████████████████████████████▍         | 18719/25000 [32:20<10:11, 10.28step/s]INFO 2026-05-20 22:54:05 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.39 loss:0.007 grdn:0.166 lr:7.5e-06 updt_s:0.088 data_s:0.008
+Training:  75%|████████████████████████████▍         | 18739/25000 [32:22<10:15, 10.17step/s]INFO 2026-05-20 22:54:07 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.40 loss:0.007 grdn:0.171 lr:7.4e-06 updt_s:0.089 data_s:0.009
+Training:  75%|████████████████████████████▌         | 18759/25000 [32:24<09:57, 10.45step/s]INFO 2026-05-20 22:54:09 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.40 loss:0.008 grdn:0.183 lr:7.4e-06 updt_s:0.086 data_s:0.008
+Training:  75%|████████████████████████████▌         | 18779/25000 [32:26<09:53, 10.49step/s]INFO 2026-05-20 22:54:11 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.41 loss:0.007 grdn:0.167 lr:7.3e-06 updt_s:0.086 data_s:0.008
+Training:  75%|████████████████████████████▌         | 18800/25000 [32:28<10:23,  9.94step/s]INFO 2026-05-20 22:54:13 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.41 loss:0.007 grdn:0.165 lr:7.3e-06 updt_s:0.088 data_s:0.012
+Training:  75%|████████████████████████████▌         | 18820/25000 [32:30<10:18,  9.99step/s]INFO 2026-05-20 22:54:15 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.42 loss:0.007 grdn:0.167 lr:7.2e-06 updt_s:0.084 data_s:0.009
+Training:  75%|████████████████████████████▋         | 18840/25000 [32:32<10:00, 10.26step/s]INFO 2026-05-20 22:54:17 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.42 loss:0.007 grdn:0.173 lr:7.2e-06 updt_s:0.091 data_s:0.008
+Training:  75%|████████████████████████████▋         | 18860/25000 [32:34<10:12, 10.02step/s]INFO 2026-05-20 22:54:19 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.43 loss:0.007 grdn:0.172 lr:7.2e-06 updt_s:0.084 data_s:0.013
+Training:  76%|████████████████████████████▋         | 18880/25000 [32:36<10:02, 10.15step/s]INFO 2026-05-20 22:54:20 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.43 loss:0.008 grdn:0.173 lr:7.1e-06 updt_s:0.088 data_s:0.009
+Training:  76%|████████████████████████████▋         | 18900/25000 [32:38<09:41, 10.50step/s]INFO 2026-05-20 22:54:22 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.44 loss:0.008 grdn:0.181 lr:7.1e-06 updt_s:0.087 data_s:0.006
+Training:  76%|████████████████████████████▊         | 18920/25000 [32:40<09:38, 10.50step/s]INFO 2026-05-20 22:54:24 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.44 loss:0.007 grdn:0.169 lr:7.0e-06 updt_s:0.088 data_s:0.006
+Training:  76%|████████████████████████████▊         | 18940/25000 [32:42<10:00, 10.09step/s]INFO 2026-05-20 22:54:26 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.44 loss:0.007 grdn:0.174 lr:7.0e-06 updt_s:0.087 data_s:0.009
+Training:  76%|████████████████████████████▊         | 18960/25000 [32:44<10:04,  9.99step/s]INFO 2026-05-20 22:54:28 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.45 loss:0.007 grdn:0.172 lr:6.9e-06 updt_s:0.089 data_s:0.009
+Training:  76%|████████████████████████████▊         | 18980/25000 [32:46<09:33, 10.49step/s]INFO 2026-05-20 22:54:30 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.45 loss:0.006 grdn:0.162 lr:6.9e-06 updt_s:0.087 data_s:0.006
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+Training:  77%|█████████████████████████████▎        | 19279/25000 [33:15<09:06, 10.46step/s]INFO 2026-05-20 22:54:59 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.52 loss:0.008 grdn:0.185 lr:6.3e-06 updt_s:0.087 data_s:0.009
+Training:  77%|█████████████████████████████▎        | 19299/25000 [33:17<09:11, 10.33step/s]INFO 2026-05-20 22:55:01 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.53 loss:0.007 grdn:0.159 lr:6.2e-06 updt_s:0.088 data_s:0.006
+Training:  77%|█████████████████████████████▎        | 19319/25000 [33:19<08:54, 10.63step/s]INFO 2026-05-20 22:55:03 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.53 loss:0.008 grdn:0.170 lr:6.2e-06 updt_s:0.095 data_s:0.006
+Training:  77%|█████████████████████████████▍        | 19339/25000 [33:21<09:23, 10.05step/s]INFO 2026-05-20 22:55:05 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.54 loss:0.007 grdn:0.172 lr:6.1e-06 updt_s:0.086 data_s:0.008
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+Training:  78%|█████████████████████████████▍        | 19380/25000 [33:25<09:19, 10.05step/s]INFO 2026-05-20 22:55:09 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.55 loss:0.007 grdn:0.168 lr:6.0e-06 updt_s:0.094 data_s:0.008
+Training:  78%|█████████████████████████████▍        | 19399/25000 [33:27<08:53, 10.49step/s]INFO 2026-05-20 22:55:11 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.55 loss:0.007 grdn:0.166 lr:6.0e-06 updt_s:0.086 data_s:0.006
+Training:  78%|█████████████████████████████▌        | 19419/25000 [33:29<08:49, 10.54step/s]INFO 2026-05-20 22:55:13 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.56 loss:0.008 grdn:0.182 lr:6.0e-06 updt_s:0.086 data_s:0.009
+Training:  78%|█████████████████████████████▌        | 19439/25000 [33:31<09:00, 10.29step/s]INFO 2026-05-20 22:55:15 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.56 loss:0.007 grdn:0.163 lr:5.9e-06 updt_s:0.090 data_s:0.008
+Training:  78%|█████████████████████████████▌        | 19459/25000 [33:33<09:12, 10.04step/s]INFO 2026-05-20 22:55:17 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.57 loss:0.008 grdn:0.183 lr:5.9e-06 updt_s:0.093 data_s:0.006
+Training:  78%|█████████████████████████████▌        | 19479/25000 [33:35<09:02, 10.17step/s]INFO 2026-05-20 22:55:19 ot_train.py:488 step:19K smpl:1M ep:3K epch:4.57 loss:0.006 grdn:0.161 lr:5.8e-06 updt_s:0.086 data_s:0.012
+Training:  78%|█████████████████████████████▋        | 19499/25000 [33:37<08:35, 10.68step/s]INFO 2026-05-20 22:55:21 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.58 loss:0.007 grdn:0.177 lr:5.8e-06 updt_s:0.086 data_s:0.008
+Training:  78%|█████████████████████████████▋        | 19520/25000 [33:39<08:49, 10.36step/s]INFO 2026-05-20 22:55:23 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.58 loss:0.007 grdn:0.180 lr:5.8e-06 updt_s:0.087 data_s:0.010
+Training:  78%|█████████████████████████████▋        | 19540/25000 [33:41<09:00, 10.10step/s]INFO 2026-05-20 22:55:25 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.59 loss:0.007 grdn:0.173 lr:5.7e-06 updt_s:0.087 data_s:0.008
+Training:  78%|█████████████████████████████▋        | 19559/25000 [33:42<09:11,  9.87step/s]INFO 2026-05-20 22:55:27 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.59 loss:0.007 grdn:0.177 lr:5.7e-06 updt_s:0.089 data_s:0.009
+Training:  78%|█████████████████████████████▊        | 19579/25000 [33:44<08:33, 10.57step/s]INFO 2026-05-20 22:55:29 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.60 loss:0.007 grdn:0.186 lr:5.6e-06 updt_s:0.085 data_s:0.009
+Training:  78%|█████████████████████████████▊        | 19599/25000 [33:46<08:39, 10.39step/s]INFO 2026-05-20 22:55:31 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.60 loss:0.007 grdn:0.167 lr:5.6e-06 updt_s:0.089 data_s:0.008
+Training:  78%|█████████████████████████████▊        | 19619/25000 [33:48<08:48, 10.19step/s]INFO 2026-05-20 22:55:33 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.60 loss:0.007 grdn:0.174 lr:5.6e-06 updt_s:0.090 data_s:0.006
+Training:  79%|█████████████████████████████▊        | 19639/25000 [33:50<08:56, 10.00step/s]INFO 2026-05-20 22:55:35 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.61 loss:0.007 grdn:0.172 lr:5.5e-06 updt_s:0.092 data_s:0.008
+Training:  79%|█████████████████████████████▉        | 19659/25000 [33:52<08:34, 10.39step/s]INFO 2026-05-20 22:55:37 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.61 loss:0.007 grdn:0.172 lr:5.5e-06 updt_s:0.089 data_s:0.008
+Training:  79%|█████████████████████████████▉        | 19679/25000 [33:54<08:25, 10.52step/s]INFO 2026-05-20 22:55:39 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.62 loss:0.007 grdn:0.170 lr:5.4e-06 updt_s:0.088 data_s:0.008
+Training:  79%|█████████████████████████████▉        | 19700/25000 [33:56<09:01,  9.78step/s]INFO 2026-05-20 22:55:41 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.62 loss:0.008 grdn:0.188 lr:5.4e-06 updt_s:0.090 data_s:0.006
+Training:  79%|█████████████████████████████▉        | 19720/25000 [33:58<08:02, 10.93step/s]INFO 2026-05-20 22:55:43 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.63 loss:0.008 grdn:0.174 lr:5.4e-06 updt_s:0.086 data_s:0.008
+Training:  79%|██████████████████████████████        | 19740/25000 [34:00<08:44, 10.02step/s]INFO 2026-05-20 22:55:45 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.63 loss:0.008 grdn:0.181 lr:5.3e-06 updt_s:0.091 data_s:0.007
+Training:  79%|██████████████████████████████        | 19760/25000 [34:02<08:31, 10.25step/s]INFO 2026-05-20 22:55:47 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.64 loss:0.007 grdn:0.193 lr:5.3e-06 updt_s:0.089 data_s:0.009
+Training:  79%|██████████████████████████████        | 19780/25000 [34:04<08:55,  9.75step/s]INFO 2026-05-20 22:55:49 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.64 loss:0.008 grdn:0.192 lr:5.2e-06 updt_s:0.086 data_s:0.010
+Training:  79%|██████████████████████████████        | 19800/25000 [34:06<08:14, 10.52step/s]INFO 2026-05-20 22:55:50 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.65 loss:0.007 grdn:0.175 lr:5.2e-06 updt_s:0.090 data_s:0.006
+Training:  79%|██████████████████████████████▏       | 19820/25000 [34:08<08:10, 10.55step/s]INFO 2026-05-20 22:55:52 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.65 loss:0.007 grdn:0.169 lr:5.2e-06 updt_s:0.085 data_s:0.012
+Training:  79%|██████████████████████████████▏       | 19840/25000 [34:10<07:48, 11.01step/s]INFO 2026-05-20 22:55:54 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.66 loss:0.008 grdn:0.180 lr:5.1e-06 updt_s:0.090 data_s:0.007
+Training:  79%|██████████████████████████████▏       | 19860/25000 [34:12<08:22, 10.22step/s]INFO 2026-05-20 22:55:56 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.66 loss:0.009 grdn:0.188 lr:5.1e-06 updt_s:0.088 data_s:0.011
+Training:  80%|██████████████████████████████▏       | 19880/25000 [34:14<07:56, 10.74step/s]INFO 2026-05-20 22:55:58 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.67 loss:0.007 grdn:0.160 lr:5.1e-06 updt_s:0.083 data_s:0.010
+Training:  80%|██████████████████████████████▏       | 19900/25000 [34:16<08:07, 10.46step/s]INFO 2026-05-20 22:56:00 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.67 loss:0.007 grdn:0.174 lr:5.0e-06 updt_s:0.092 data_s:0.007
+Training:  80%|██████████████████████████████▎       | 19920/25000 [34:18<07:52, 10.76step/s]INFO 2026-05-20 22:56:02 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.67 loss:0.007 grdn:0.168 lr:5.0e-06 updt_s:0.086 data_s:0.008
+Training:  80%|██████████████████████████████▎       | 19940/25000 [34:20<07:55, 10.64step/s]INFO 2026-05-20 22:56:04 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.68 loss:0.008 grdn:0.192 lr:4.9e-06 updt_s:0.090 data_s:0.009
+Training:  80%|██████████████████████████████▎       | 19960/25000 [34:22<08:05, 10.38step/s]INFO 2026-05-20 22:56:06 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.68 loss:0.007 grdn:0.166 lr:4.9e-06 updt_s:0.089 data_s:0.007
+Training:  80%|██████████████████████████████▎       | 19979/25000 [34:24<07:43, 10.82step/s]INFO 2026-05-20 22:56:08 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.69 loss:0.007 grdn:0.185 lr:4.9e-06 updt_s:0.091 data_s:0.006
+Training:  80%|██████████████████████████████▍       | 20000/25000 [34:26<09:17,  8.97step/s]INFO 2026-05-20 22:56:10 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.69 loss:0.007 grdn:0.157 lr:4.8e-06 updt_s:0.091 data_s:0.007
+INFO 2026-05-20 22:56:10 ot_train.py:502 Checkpoint policy after step 20000
+Training:  80%|██████████████████████████████▍       | 20019/25000 [34:36<11:50,  7.01step/s]INFO 2026-05-20 22:56:21 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.70 loss:0.007 grdn:0.169 lr:4.8e-06 updt_s:0.073 data_s:0.006
+Training:  80%|██████████████████████████████▍       | 20040/25000 [34:38<08:00, 10.32step/s]INFO 2026-05-20 22:56:23 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.70 loss:0.007 grdn:0.170 lr:4.8e-06 updt_s:0.089 data_s:0.009
+Training:  80%|██████████████████████████████▍       | 20060/25000 [34:40<08:13, 10.01step/s]INFO 2026-05-20 22:56:25 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.71 loss:0.008 grdn:0.191 lr:4.7e-06 updt_s:0.086 data_s:0.008
+Training:  80%|██████████████████████████████▌       | 20080/25000 [34:42<07:58, 10.28step/s]INFO 2026-05-20 22:56:26 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.71 loss:0.006 grdn:0.157 lr:4.7e-06 updt_s:0.082 data_s:0.009
+Training:  80%|██████████████████████████████▌       | 20100/25000 [34:44<07:51, 10.38step/s]INFO 2026-05-20 22:56:28 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.72 loss:0.007 grdn:0.189 lr:4.6e-06 updt_s:0.088 data_s:0.006
+Training:  80%|██████████████████████████████▌       | 20120/25000 [34:46<07:59, 10.18step/s]INFO 2026-05-20 22:56:30 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.72 loss:0.008 grdn:0.177 lr:4.6e-06 updt_s:0.087 data_s:0.007
+Training:  81%|██████████████████████████████▌       | 20140/25000 [34:48<07:23, 10.96step/s]INFO 2026-05-20 22:56:32 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.73 loss:0.007 grdn:0.167 lr:4.6e-06 updt_s:0.084 data_s:0.006
+Training:  81%|██████████████████████████████▋       | 20160/25000 [34:50<07:50, 10.28step/s]INFO 2026-05-20 22:56:34 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.73 loss:0.007 grdn:0.176 lr:4.5e-06 updt_s:0.088 data_s:0.011
+Training:  81%|██████████████████████████████▋       | 20180/25000 [34:52<07:25, 10.83step/s]INFO 2026-05-20 22:56:36 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.74 loss:0.007 grdn:0.158 lr:4.5e-06 updt_s:0.085 data_s:0.009
+Training:  81%|██████████████████████████████▋       | 20200/25000 [34:54<08:14,  9.72step/s]INFO 2026-05-20 22:56:38 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.74 loss:0.007 grdn:0.175 lr:4.5e-06 updt_s:0.088 data_s:0.009
+Training:  81%|██████████████████████████████▋       | 20219/25000 [34:56<07:30, 10.62step/s]INFO 2026-05-20 22:56:40 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.75 loss:0.007 grdn:0.175 lr:4.4e-06 updt_s:0.083 data_s:0.009
+Training:  81%|██████████████████████████████▊       | 20239/25000 [34:57<07:33, 10.51step/s]INFO 2026-05-20 22:56:42 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.75 loss:0.007 grdn:0.172 lr:4.4e-06 updt_s:0.087 data_s:0.006
+Training:  81%|██████████████████████████████▊       | 20259/25000 [34:59<07:39, 10.31step/s]INFO 2026-05-20 22:56:44 ot_train.py:488 step:20K smpl:1M ep:3K epch:4.75 loss:0.007 grdn:0.163 lr:4.4e-06 updt_s:0.084 data_s:0.008
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+Training:  86%|████████████████████████████████▊     | 21599/25000 [37:12<05:21, 10.57step/s]INFO 2026-05-20 22:58:56 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.07 loss:0.007 grdn:0.167 lr:2.3e-06 updt_s:0.090 data_s:0.007
+Training:  86%|████████████████████████████████▊     | 21619/25000 [37:14<05:14, 10.76step/s]INFO 2026-05-20 22:58:58 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.07 loss:0.007 grdn:0.167 lr:2.3e-06 updt_s:0.087 data_s:0.008
+Training:  87%|████████████████████████████████▉     | 21640/25000 [37:16<05:25, 10.31step/s]INFO 2026-05-20 22:59:00 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.08 loss:0.007 grdn:0.169 lr:2.2e-06 updt_s:0.090 data_s:0.010
+Training:  87%|████████████████████████████████▉     | 21660/25000 [37:18<05:31, 10.09step/s]INFO 2026-05-20 22:59:02 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.08 loss:0.007 grdn:0.179 lr:2.2e-06 updt_s:0.086 data_s:0.011
+Training:  87%|████████████████████████████████▉     | 21679/25000 [37:20<04:53, 11.32step/s]INFO 2026-05-20 22:59:04 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.09 loss:0.007 grdn:0.165 lr:2.2e-06 updt_s:0.089 data_s:0.006
+Training:  87%|████████████████████████████████▉     | 21699/25000 [37:22<05:21, 10.27step/s]INFO 2026-05-20 22:59:06 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.09 loss:0.006 grdn:0.164 lr:2.1e-06 updt_s:0.089 data_s:0.009
+Training:  87%|█████████████████████████████████     | 21719/25000 [37:24<05:15, 10.41step/s]INFO 2026-05-20 22:59:08 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.10 loss:0.007 grdn:0.161 lr:2.1e-06 updt_s:0.092 data_s:0.006
+Training:  87%|█████████████████████████████████     | 21740/25000 [37:26<04:43, 11.50step/s]INFO 2026-05-20 22:59:10 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.10 loss:0.007 grdn:0.184 lr:2.1e-06 updt_s:0.087 data_s:0.008
+Training:  87%|█████████████████████████████████     | 21760/25000 [37:28<05:18, 10.19step/s]INFO 2026-05-20 22:59:12 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.11 loss:0.007 grdn:0.183 lr:2.1e-06 updt_s:0.088 data_s:0.007
+Training:  87%|█████████████████████████████████     | 21780/25000 [37:30<05:10, 10.37step/s]INFO 2026-05-20 22:59:14 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.11 loss:0.007 grdn:0.168 lr:2.0e-06 updt_s:0.088 data_s:0.008
+Training:  87%|█████████████████████████████████▏    | 21800/25000 [37:32<05:35,  9.54step/s]INFO 2026-05-20 22:59:16 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.12 loss:0.008 grdn:0.168 lr:2.0e-06 updt_s:0.088 data_s:0.011
+Training:  87%|█████████████████████████████████▏    | 21819/25000 [37:33<04:52, 10.87step/s]INFO 2026-05-20 22:59:18 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.12 loss:0.008 grdn:0.186 lr:2.0e-06 updt_s:0.082 data_s:0.009
+Training:  87%|█████████████████████████████████▏    | 21839/25000 [37:36<05:09, 10.21step/s]INFO 2026-05-20 22:59:20 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.13 loss:0.009 grdn:0.183 lr:2.0e-06 updt_s:0.092 data_s:0.009
+Training:  87%|█████████████████████████████████▏    | 21859/25000 [37:37<04:51, 10.79step/s]INFO 2026-05-20 22:59:22 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.13 loss:0.007 grdn:0.181 lr:1.9e-06 updt_s:0.084 data_s:0.006
+Training:  88%|█████████████████████████████████▎    | 21879/25000 [37:39<04:43, 11.01step/s]INFO 2026-05-20 22:59:24 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.13 loss:0.007 grdn:0.164 lr:1.9e-06 updt_s:0.090 data_s:0.008
+Training:  88%|█████████████████████████████████▎    | 21900/25000 [37:41<05:04, 10.19step/s]INFO 2026-05-20 22:59:26 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.14 loss:0.007 grdn:0.170 lr:1.9e-06 updt_s:0.093 data_s:0.007
+Training:  88%|█████████████████████████████████▎    | 21920/25000 [37:43<04:49, 10.65step/s]INFO 2026-05-20 22:59:28 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.14 loss:0.008 grdn:0.172 lr:1.9e-06 updt_s:0.084 data_s:0.008
+Training:  88%|█████████████████████████████████▎    | 21940/25000 [37:45<05:15,  9.70step/s]INFO 2026-05-20 22:59:30 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.15 loss:0.006 grdn:0.155 lr:1.9e-06 updt_s:0.094 data_s:0.007
+Training:  88%|█████████████████████████████████▍    | 21960/25000 [37:47<04:48, 10.53step/s]INFO 2026-05-20 22:59:32 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.15 loss:0.008 grdn:0.195 lr:1.8e-06 updt_s:0.084 data_s:0.008
+Training:  88%|█████████████████████████████████▍    | 21980/25000 [37:49<04:40, 10.78step/s]INFO 2026-05-20 22:59:34 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.16 loss:0.008 grdn:0.173 lr:1.8e-06 updt_s:0.086 data_s:0.009
+Training:  88%|█████████████████████████████████▍    | 22000/25000 [37:51<04:46, 10.47step/s]INFO 2026-05-20 22:59:36 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.16 loss:0.008 grdn:0.184 lr:1.8e-06 updt_s:0.092 data_s:0.007
+INFO 2026-05-20 22:59:36 ot_train.py:502 Checkpoint policy after step 22000
+Training:  88%|█████████████████████████████████▍    | 22020/25000 [38:02<07:04,  7.02step/s]INFO 2026-05-20 22:59:46 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.17 loss:0.007 grdn:0.171 lr:1.8e-06 updt_s:0.072 data_s:0.006
+Training:  88%|█████████████████████████████████▌    | 22040/25000 [38:04<05:03,  9.76step/s]INFO 2026-05-20 22:59:48 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.17 loss:0.007 grdn:0.166 lr:1.7e-06 updt_s:0.093 data_s:0.009
+Training:  88%|█████████████████████████████████▌    | 22060/25000 [38:06<04:20, 11.31step/s]INFO 2026-05-20 22:59:50 ot_train.py:488 step:22K smpl:1M ep:3K epch:5.18 loss:0.007 grdn:0.156 lr:1.7e-06 updt_s:0.084 data_s:0.007
+Training:  88%|█████████████████████████████████▌    | 22080/25000 [38:08<04:44, 10.27step/s]INFO 2026-05-20 22:59:52 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.18 loss:0.007 grdn:0.176 lr:1.7e-06 updt_s:0.088 data_s:0.009
+Training:  88%|█████████████████████████████████▌    | 22100/25000 [38:10<04:32, 10.63step/s]INFO 2026-05-20 22:59:54 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.19 loss:0.008 grdn:0.178 lr:1.7e-06 updt_s:0.087 data_s:0.007
+Training:  88%|█████████████████████████████████▌    | 22120/25000 [38:12<04:37, 10.37step/s]INFO 2026-05-20 22:59:56 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.19 loss:0.008 grdn:0.194 lr:1.6e-06 updt_s:0.088 data_s:0.008
+Training:  89%|█████████████████████████████████▋    | 22140/25000 [38:14<04:53,  9.75step/s]INFO 2026-05-20 22:59:58 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.20 loss:0.007 grdn:0.171 lr:1.6e-06 updt_s:0.088 data_s:0.009
+Training:  89%|█████████████████████████████████▋    | 22160/25000 [38:15<04:21, 10.87step/s]INFO 2026-05-20 23:00:00 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.20 loss:0.007 grdn:0.155 lr:1.6e-06 updt_s:0.084 data_s:0.008
+Training:  89%|█████████████████████████████████▋    | 22180/25000 [38:17<04:34, 10.28step/s]INFO 2026-05-20 23:00:02 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.21 loss:0.007 grdn:0.172 lr:1.6e-06 updt_s:0.093 data_s:0.006
+Training:  89%|█████████████████████████████████▋    | 22200/25000 [38:19<04:17, 10.86step/s]INFO 2026-05-20 23:00:04 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.21 loss:0.008 grdn:0.183 lr:1.6e-06 updt_s:0.082 data_s:0.009
+Training:  89%|█████████████████████████████████▊    | 22220/25000 [38:21<04:12, 11.00step/s]INFO 2026-05-20 23:00:06 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.21 loss:0.007 grdn:0.174 lr:1.5e-06 updt_s:0.087 data_s:0.010
+Training:  89%|█████████████████████████████████▊    | 22240/25000 [38:23<04:30, 10.19step/s]INFO 2026-05-20 23:00:07 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.22 loss:0.007 grdn:0.165 lr:1.5e-06 updt_s:0.083 data_s:0.008
+Training:  89%|█████████████████████████████████▊    | 22260/25000 [38:25<04:07, 11.06step/s]INFO 2026-05-20 23:00:09 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.22 loss:0.007 grdn:0.181 lr:1.5e-06 updt_s:0.093 data_s:0.006
+Training:  89%|█████████████████████████████████▊    | 22280/25000 [38:27<04:39,  9.72step/s]INFO 2026-05-20 23:00:11 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.23 loss:0.007 grdn:0.170 lr:1.5e-06 updt_s:0.085 data_s:0.014
+Training:  89%|█████████████████████████████████▉    | 22300/25000 [38:29<04:13, 10.66step/s]INFO 2026-05-20 23:00:13 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.23 loss:0.006 grdn:0.161 lr:1.4e-06 updt_s:0.084 data_s:0.006
+Training:  89%|█████████████████████████████████▉    | 22320/25000 [38:31<04:16, 10.46step/s]INFO 2026-05-20 23:00:15 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.24 loss:0.007 grdn:0.169 lr:1.4e-06 updt_s:0.094 data_s:0.007
+Training:  89%|█████████████████████████████████▉    | 22340/25000 [38:33<04:17, 10.33step/s]INFO 2026-05-20 23:00:17 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.24 loss:0.007 grdn:0.170 lr:1.4e-06 updt_s:0.087 data_s:0.008
+Training:  89%|█████████████████████████████████▉    | 22360/25000 [38:35<04:08, 10.63step/s]INFO 2026-05-20 23:00:19 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.25 loss:0.008 grdn:0.176 lr:1.4e-06 updt_s:0.086 data_s:0.009
+Training:  90%|██████████████████████████████████    | 22380/25000 [38:37<04:07, 10.60step/s]INFO 2026-05-20 23:00:21 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.25 loss:0.007 grdn:0.181 lr:1.4e-06 updt_s:0.084 data_s:0.009
+Training:  90%|██████████████████████████████████    | 22400/25000 [38:39<04:06, 10.54step/s]INFO 2026-05-20 23:00:23 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.26 loss:0.007 grdn:0.175 lr:1.3e-06 updt_s:0.086 data_s:0.007
+Training:  90%|██████████████████████████████████    | 22420/25000 [38:41<04:15, 10.09step/s]INFO 2026-05-20 23:00:25 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.26 loss:0.008 grdn:0.184 lr:1.3e-06 updt_s:0.087 data_s:0.008
+Training:  90%|██████████████████████████████████    | 22440/25000 [38:42<03:57, 10.76step/s]INFO 2026-05-20 23:00:27 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.27 loss:0.008 grdn:0.165 lr:1.3e-06 updt_s:0.087 data_s:0.007
+Training:  90%|██████████████████████████████████▏   | 22460/25000 [38:44<03:59, 10.61step/s]INFO 2026-05-20 23:00:29 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.27 loss:0.007 grdn:0.177 lr:1.3e-06 updt_s:0.087 data_s:0.007
+Training:  90%|██████████████████████████████████▏   | 22480/25000 [38:46<04:06, 10.24step/s]INFO 2026-05-20 23:00:31 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.28 loss:0.007 grdn:0.173 lr:1.3e-06 updt_s:0.086 data_s:0.010
+Training:  90%|██████████████████████████████████▏   | 22500/25000 [38:48<04:17,  9.70step/s]INFO 2026-05-20 23:00:33 ot_train.py:488 step:22K smpl:1M ep:4K epch:5.28 loss:0.007 grdn:0.169 lr:1.2e-06 updt_s:0.090 data_s:0.008
+Training:  90%|██████████████████████████████████▏   | 22520/25000 [38:50<03:57, 10.45step/s]INFO 2026-05-20 23:00:35 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.29 loss:0.006 grdn:0.153 lr:1.2e-06 updt_s:0.090 data_s:0.008
+Training:  90%|██████████████████████████████████▎   | 22540/25000 [38:52<03:58, 10.32step/s]INFO 2026-05-20 23:00:37 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.29 loss:0.007 grdn:0.175 lr:1.2e-06 updt_s:0.087 data_s:0.006
+Training:  90%|██████████████████████████████████▎   | 22560/25000 [38:54<03:56, 10.33step/s]INFO 2026-05-20 23:00:38 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.29 loss:0.007 grdn:0.171 lr:1.2e-06 updt_s:0.088 data_s:0.007
+Training:  90%|██████████████████████████████████▎   | 22579/25000 [38:56<04:04,  9.91step/s]INFO 2026-05-20 23:00:40 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.30 loss:0.007 grdn:0.164 lr:1.2e-06 updt_s:0.094 data_s:0.008
+Training:  90%|██████████████████████████████████▎   | 22599/25000 [38:58<03:49, 10.44step/s]INFO 2026-05-20 23:00:42 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.30 loss:0.008 grdn:0.183 lr:1.1e-06 updt_s:0.090 data_s:0.005
+Training:  90%|██████████████████████████████████▍   | 22620/25000 [39:00<03:53, 10.18step/s]INFO 2026-05-20 23:00:44 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.31 loss:0.008 grdn:0.180 lr:1.1e-06 updt_s:0.088 data_s:0.010
+Training:  91%|██████████████████████████████████▍   | 22640/25000 [39:02<03:47, 10.38step/s]INFO 2026-05-20 23:00:46 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.31 loss:0.007 grdn:0.174 lr:1.1e-06 updt_s:0.085 data_s:0.011
+Training:  91%|██████████████████████████████████▍   | 22660/25000 [39:04<03:57,  9.86step/s]INFO 2026-05-20 23:00:48 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.32 loss:0.008 grdn:0.181 lr:1.1e-06 updt_s:0.087 data_s:0.007
+Training:  91%|██████████████████████████████████▍   | 22679/25000 [39:06<03:48, 10.14step/s]INFO 2026-05-20 23:00:50 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.32 loss:0.007 grdn:0.169 lr:1.1e-06 updt_s:0.090 data_s:0.008
+Training:  91%|██████████████████████████████████▌   | 22699/25000 [39:08<03:33, 10.79step/s]INFO 2026-05-20 23:00:52 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.33 loss:0.008 grdn:0.185 lr:1.1e-06 updt_s:0.084 data_s:0.007
+Training:  91%|██████████████████████████████████▌   | 22720/25000 [39:10<03:45, 10.12step/s]INFO 2026-05-20 23:00:54 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.33 loss:0.006 grdn:0.173 lr:1.0e-06 updt_s:0.092 data_s:0.009
+Training:  91%|██████████████████████████████████▌   | 22740/25000 [39:12<03:38, 10.35step/s]INFO 2026-05-20 23:00:56 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.34 loss:0.007 grdn:0.168 lr:1.0e-06 updt_s:0.087 data_s:0.006
+Training:  91%|██████████████████████████████████▌   | 22760/25000 [39:14<03:30, 10.63step/s]INFO 2026-05-20 23:00:58 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.34 loss:0.006 grdn:0.168 lr:1.0e-06 updt_s:0.088 data_s:0.006
+Training:  91%|██████████████████████████████████▌   | 22779/25000 [39:15<03:33, 10.39step/s]INFO 2026-05-20 23:01:00 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.35 loss:0.007 grdn:0.171 lr:9.8e-07 updt_s:0.089 data_s:0.006
+Training:  91%|██████████████████████████████████▋   | 22799/25000 [39:17<03:38, 10.06step/s]INFO 2026-05-20 23:01:02 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.35 loss:0.007 grdn:0.182 lr:9.7e-07 updt_s:0.085 data_s:0.012
+Training:  91%|██████████████████████████████████▋   | 22819/25000 [39:19<03:25, 10.59step/s]INFO 2026-05-20 23:01:04 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.36 loss:0.007 grdn:0.157 lr:9.5e-07 updt_s:0.082 data_s:0.009
+Training:  91%|██████████████████████████████████▋   | 22840/25000 [39:21<03:28, 10.36step/s]INFO 2026-05-20 23:01:06 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.36 loss:0.008 grdn:0.176 lr:9.3e-07 updt_s:0.085 data_s:0.014
+Training:  91%|██████████████████████████████████▋   | 22860/25000 [39:23<03:28, 10.26step/s]INFO 2026-05-20 23:01:08 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.36 loss:0.007 grdn:0.159 lr:9.1e-07 updt_s:0.089 data_s:0.008
+Training:  92%|██████████████████████████████████▊   | 22880/25000 [39:25<03:30, 10.08step/s]INFO 2026-05-20 23:01:10 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.37 loss:0.007 grdn:0.175 lr:9.0e-07 updt_s:0.090 data_s:0.006
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+Training:  92%|██████████████████████████████████▊   | 22939/25000 [39:31<03:15, 10.56step/s]INFO 2026-05-20 23:01:15 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.38 loss:0.007 grdn:0.177 lr:8.5e-07 updt_s:0.087 data_s:0.011
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+Training:  92%|██████████████████████████████████▉   | 22979/25000 [39:35<03:13, 10.46step/s]INFO 2026-05-20 23:01:19 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.39 loss:0.007 grdn:0.170 lr:8.2e-07 updt_s:0.088 data_s:0.009
+Training:  92%|██████████████████████████████████▉   | 22999/25000 [39:37<03:09, 10.57step/s]INFO 2026-05-20 23:01:21 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.40 loss:0.008 grdn:0.182 lr:8.0e-07 updt_s:0.087 data_s:0.008
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+Training:  92%|███████████████████████████████████   | 23039/25000 [39:41<03:13, 10.13step/s]INFO 2026-05-20 23:01:25 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.41 loss:0.005 grdn:0.148 lr:7.7e-07 updt_s:0.090 data_s:0.009
+Training:  92%|███████████████████████████████████   | 23059/25000 [39:43<02:59, 10.84step/s]INFO 2026-05-20 23:01:27 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.41 loss:0.009 grdn:0.198 lr:7.5e-07 updt_s:0.087 data_s:0.007
+Training:  92%|███████████████████████████████████   | 23079/25000 [39:45<03:16,  9.77step/s]INFO 2026-05-20 23:01:29 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.42 loss:0.008 grdn:0.193 lr:7.4e-07 updt_s:0.089 data_s:0.011
+Training:  92%|███████████████████████████████████   | 23099/25000 [39:47<03:01, 10.45step/s]INFO 2026-05-20 23:01:31 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.42 loss:0.007 grdn:0.173 lr:7.2e-07 updt_s:0.087 data_s:0.008
+Training:  92%|███████████████████████████████████▏  | 23120/25000 [39:49<03:06, 10.06step/s]INFO 2026-05-20 23:01:33 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.43 loss:0.008 grdn:0.172 lr:7.1e-07 updt_s:0.085 data_s:0.015
+Training:  93%|███████████████████████████████████▏  | 23140/25000 [39:51<03:02, 10.21step/s]INFO 2026-05-20 23:01:35 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.43 loss:0.008 grdn:0.180 lr:6.9e-07 updt_s:0.089 data_s:0.008
+Training:  93%|███████████████████████████████████▏  | 23160/25000 [39:53<02:56, 10.44step/s]INFO 2026-05-20 23:01:37 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.44 loss:0.008 grdn:0.177 lr:6.8e-07 updt_s:0.087 data_s:0.008
+Training:  93%|███████████████████████████████████▏  | 23180/25000 [39:55<02:56, 10.34step/s]INFO 2026-05-20 23:01:39 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.44 loss:0.007 grdn:0.168 lr:6.6e-07 updt_s:0.090 data_s:0.006
+Training:  93%|███████████████████████████████████▎  | 23200/25000 [39:57<02:57, 10.14step/s]INFO 2026-05-20 23:01:41 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.44 loss:0.007 grdn:0.168 lr:6.5e-07 updt_s:0.089 data_s:0.006
+Training:  93%|███████████████████████████████████▎  | 23220/25000 [39:58<02:54, 10.21step/s]INFO 2026-05-20 23:01:43 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.45 loss:0.006 grdn:0.160 lr:6.3e-07 updt_s:0.089 data_s:0.006
+Training:  93%|███████████████████████████████████▎  | 23240/25000 [40:00<02:41, 10.92step/s]INFO 2026-05-20 23:01:45 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.45 loss:0.008 grdn:0.175 lr:6.2e-07 updt_s:0.086 data_s:0.006
+Training:  93%|███████████████████████████████████▎  | 23259/25000 [40:02<02:45, 10.49step/s]INFO 2026-05-20 23:01:47 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.46 loss:0.007 grdn:0.169 lr:6.1e-07 updt_s:0.088 data_s:0.012
+Training:  93%|███████████████████████████████████▍  | 23279/25000 [40:04<02:38, 10.85step/s]INFO 2026-05-20 23:01:49 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.46 loss:0.007 grdn:0.176 lr:5.9e-07 updt_s:0.090 data_s:0.006
+Training:  93%|███████████████████████████████████▍  | 23299/25000 [40:06<02:40, 10.63step/s]INFO 2026-05-20 23:01:51 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.47 loss:0.007 grdn:0.168 lr:5.8e-07 updt_s:0.090 data_s:0.007
+Training:  93%|███████████████████████████████████▍  | 23319/25000 [40:08<02:37, 10.64step/s]INFO 2026-05-20 23:01:52 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.47 loss:0.006 grdn:0.165 lr:5.7e-07 updt_s:0.084 data_s:0.008
+Training:  93%|███████████████████████████████████▍  | 23340/25000 [40:10<02:41, 10.26step/s]INFO 2026-05-20 23:01:54 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.48 loss:0.007 grdn:0.174 lr:5.5e-07 updt_s:0.089 data_s:0.008
+Training:  93%|███████████████████████████████████▌  | 23360/25000 [40:12<02:38, 10.34step/s]INFO 2026-05-20 23:01:56 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.48 loss:0.008 grdn:0.184 lr:5.4e-07 updt_s:0.090 data_s:0.006
+Training:  94%|███████████████████████████████████▌  | 23380/25000 [40:14<02:30, 10.79step/s]INFO 2026-05-20 23:01:58 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.49 loss:0.007 grdn:0.177 lr:5.3e-07 updt_s:0.087 data_s:0.006
+Training:  94%|███████████████████████████████████▌  | 23400/25000 [40:16<02:46,  9.61step/s]INFO 2026-05-20 23:02:00 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.49 loss:0.006 grdn:0.158 lr:5.1e-07 updt_s:0.091 data_s:0.008
+Training:  94%|███████████████████████████████████▌  | 23420/25000 [40:18<02:46,  9.49step/s]INFO 2026-05-20 23:02:02 ot_train.py:488 step:23K smpl:1M ep:4K epch:5.50 loss:0.007 grdn:0.184 lr:5.0e-07 updt_s:0.092 data_s:0.006
+Training:  94%|███████████████████████████████████▋  | 23439/25000 [40:20<02:29, 10.41step/s]INFO 2026-05-20 23:02:04 ot_train.py:488 step:23K smpl:2M ep:4K epch:5.50 loss:0.007 grdn:0.170 lr:4.9e-07 updt_s:0.088 data_s:0.007
+Training:  94%|███████████████████████████████████▋  | 23459/25000 [40:22<02:35,  9.93step/s]INFO 2026-05-20 23:02:06 ot_train.py:488 step:23K smpl:2M ep:4K epch:5.51 loss:0.006 grdn:0.168 lr:4.8e-07 updt_s:0.091 data_s:0.008
+Training:  94%|███████████████████████████████████▋  | 23480/25000 [40:24<02:34,  9.85step/s]INFO 2026-05-20 23:02:08 ot_train.py:488 step:23K smpl:2M ep:4K epch:5.51 loss:0.006 grdn:0.163 lr:4.6e-07 updt_s:0.090 data_s:0.007
+Training:  94%|███████████████████████████████████▋  | 23500/25000 [40:26<02:30,  9.98step/s]INFO 2026-05-20 23:02:10 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.52 loss:0.007 grdn:0.169 lr:4.5e-07 updt_s:0.090 data_s:0.006
+Training:  94%|███████████████████████████████████▊  | 23520/25000 [40:28<02:25, 10.20step/s]INFO 2026-05-20 23:02:12 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.52 loss:0.007 grdn:0.178 lr:4.4e-07 updt_s:0.089 data_s:0.009
+Training:  94%|███████████████████████████████████▊  | 23539/25000 [40:30<02:17, 10.66step/s]INFO 2026-05-20 23:02:14 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.52 loss:0.007 grdn:0.177 lr:4.3e-07 updt_s:0.086 data_s:0.007
+Training:  94%|███████████████████████████████████▊  | 23559/25000 [40:32<02:12, 10.89step/s]INFO 2026-05-20 23:02:16 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.53 loss:0.007 grdn:0.178 lr:4.2e-07 updt_s:0.087 data_s:0.013
+Training:  94%|███████████████████████████████████▊  | 23579/25000 [40:34<02:26,  9.67step/s]INFO 2026-05-20 23:02:18 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.53 loss:0.008 grdn:0.179 lr:4.1e-07 updt_s:0.087 data_s:0.009
+Training:  94%|███████████████████████████████████▊  | 23600/25000 [40:36<02:15, 10.33step/s]INFO 2026-05-20 23:02:20 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.54 loss:0.007 grdn:0.175 lr:3.9e-07 updt_s:0.088 data_s:0.007
+Training:  94%|███████████████████████████████████▉  | 23620/25000 [40:38<02:14, 10.27step/s]INFO 2026-05-20 23:02:22 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.54 loss:0.006 grdn:0.171 lr:3.8e-07 updt_s:0.087 data_s:0.009
+Training:  95%|███████████████████████████████████▉  | 23640/25000 [40:40<02:05, 10.82step/s]INFO 2026-05-20 23:02:24 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.55 loss:0.007 grdn:0.175 lr:3.7e-07 updt_s:0.090 data_s:0.006
+Training:  95%|███████████████████████████████████▉  | 23660/25000 [40:41<02:02, 10.91step/s]INFO 2026-05-20 23:02:26 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.55 loss:0.006 grdn:0.162 lr:3.6e-07 updt_s:0.087 data_s:0.008
+Training:  95%|███████████████████████████████████▉  | 23680/25000 [40:43<02:09, 10.18step/s]INFO 2026-05-20 23:02:28 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.56 loss:0.007 grdn:0.176 lr:3.5e-07 updt_s:0.092 data_s:0.009
+Training:  95%|████████████████████████████████████  | 23700/25000 [40:45<02:02, 10.58step/s]INFO 2026-05-20 23:02:30 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.56 loss:0.008 grdn:0.179 lr:3.4e-07 updt_s:0.087 data_s:0.009
+Training:  95%|████████████████████████████████████  | 23720/25000 [40:47<02:05, 10.21step/s]INFO 2026-05-20 23:02:32 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.57 loss:0.007 grdn:0.158 lr:3.3e-07 updt_s:0.086 data_s:0.008
+Training:  95%|████████████████████████████████████  | 23739/25000 [40:49<01:57, 10.75step/s]INFO 2026-05-20 23:02:34 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.57 loss:0.007 grdn:0.167 lr:3.2e-07 updt_s:0.093 data_s:0.006
+Training:  95%|████████████████████████████████████  | 23759/25000 [40:51<02:00, 10.28step/s]INFO 2026-05-20 23:02:36 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.58 loss:0.007 grdn:0.184 lr:3.1e-07 updt_s:0.087 data_s:0.008
+Training:  95%|████████████████████████████████████▏ | 23779/25000 [40:53<01:54, 10.67step/s]INFO 2026-05-20 23:02:37 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.58 loss:0.008 grdn:0.169 lr:3.0e-07 updt_s:0.089 data_s:0.006
+Training:  95%|████████████████████████████████████▏ | 23799/25000 [40:55<01:57, 10.22step/s]INFO 2026-05-20 23:02:39 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.59 loss:0.006 grdn:0.151 lr:2.9e-07 updt_s:0.087 data_s:0.009
+Training:  95%|████████████████████████████████████▏ | 23819/25000 [40:57<01:56, 10.13step/s]INFO 2026-05-20 23:02:41 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.59 loss:0.007 grdn:0.158 lr:2.8e-07 updt_s:0.088 data_s:0.008
+Training:  95%|████████████████████████████████████▏ | 23839/25000 [40:59<01:56, 10.01step/s]INFO 2026-05-20 23:02:43 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.59 loss:0.008 grdn:0.181 lr:2.7e-07 updt_s:0.092 data_s:0.006
+Training:  95%|████████████████████████████████████▎ | 23859/25000 [41:01<01:44, 10.88step/s]INFO 2026-05-20 23:02:45 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.60 loss:0.007 grdn:0.173 lr:2.6e-07 updt_s:0.088 data_s:0.009
+Training:  96%|████████████████████████████████████▎ | 23879/25000 [41:03<01:44, 10.78step/s]INFO 2026-05-20 23:02:47 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.60 loss:0.006 grdn:0.154 lr:2.5e-07 updt_s:0.085 data_s:0.009
+Training:  96%|████████████████████████████████████▎ | 23900/25000 [41:05<01:57,  9.38step/s]INFO 2026-05-20 23:02:49 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.61 loss:0.008 grdn:0.180 lr:2.4e-07 updt_s:0.094 data_s:0.008
+Training:  96%|████████████████████████████████████▎ | 23919/25000 [41:07<01:39, 10.83step/s]INFO 2026-05-20 23:02:51 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.61 loss:0.007 grdn:0.163 lr:2.4e-07 updt_s:0.084 data_s:0.011
+Training:  96%|████████████████████████████████████▍ | 23939/25000 [41:09<01:45, 10.06step/s]INFO 2026-05-20 23:02:53 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.62 loss:0.007 grdn:0.190 lr:2.3e-07 updt_s:0.086 data_s:0.010
+Training:  96%|████████████████████████████████████▍ | 23959/25000 [41:11<01:39, 10.43step/s]INFO 2026-05-20 23:02:55 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.62 loss:0.007 grdn:0.172 lr:2.2e-07 updt_s:0.087 data_s:0.011
+Training:  96%|████████████████████████████████████▍ | 23979/25000 [41:13<01:41, 10.09step/s]INFO 2026-05-20 23:02:57 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.63 loss:0.008 grdn:0.172 lr:2.1e-07 updt_s:0.088 data_s:0.008
+Training:  96%|████████████████████████████████████▍ | 24000/25000 [41:15<01:35, 10.49step/s]INFO 2026-05-20 23:02:59 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.63 loss:0.008 grdn:0.179 lr:2.0e-07 updt_s:0.088 data_s:0.006
+INFO 2026-05-20 23:02:59 ot_train.py:502 Checkpoint policy after step 24000
+Training:  96%|████████████████████████████████████▌ | 24020/25000 [41:25<02:17,  7.14step/s]INFO 2026-05-20 23:03:10 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.64 loss:0.006 grdn:0.160 lr:1.9e-07 updt_s:0.070 data_s:0.007
+Training:  96%|████████████████████████████████████▌ | 24040/25000 [41:27<01:30, 10.63step/s]INFO 2026-05-20 23:03:12 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.64 loss:0.007 grdn:0.173 lr:1.9e-07 updt_s:0.087 data_s:0.011
+Training:  96%|████████████████████████████████████▌ | 24060/25000 [41:29<01:28, 10.60step/s]INFO 2026-05-20 23:03:14 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.65 loss:0.007 grdn:0.163 lr:1.8e-07 updt_s:0.090 data_s:0.006
+Training:  96%|████████████████████████████████████▌ | 24080/25000 [41:31<01:29, 10.23step/s]INFO 2026-05-20 23:03:16 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.65 loss:0.008 grdn:0.180 lr:1.7e-07 updt_s:0.087 data_s:0.010
+Training:  96%|████████████████████████████████████▋ | 24100/25000 [41:33<01:25, 10.54step/s]INFO 2026-05-20 23:03:17 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.66 loss:0.007 grdn:0.173 lr:1.6e-07 updt_s:0.085 data_s:0.008
+Training:  96%|████████████████████████████████████▋ | 24120/25000 [41:35<01:28,  9.92step/s]INFO 2026-05-20 23:03:19 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.66 loss:0.007 grdn:0.182 lr:1.6e-07 updt_s:0.088 data_s:0.006
+Training:  97%|████████████████████████████████████▋ | 24140/25000 [41:37<01:25, 10.00step/s]INFO 2026-05-20 23:03:21 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.67 loss:0.008 grdn:0.174 lr:1.5e-07 updt_s:0.088 data_s:0.007
+Training:  97%|████████████████████████████████████▋ | 24160/25000 [41:39<01:20, 10.40step/s]INFO 2026-05-20 23:03:23 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.67 loss:0.007 grdn:0.165 lr:1.4e-07 updt_s:0.086 data_s:0.009
+Training:  97%|████████████████████████████████████▊ | 24180/25000 [41:41<01:19, 10.28step/s]INFO 2026-05-20 23:03:25 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.67 loss:0.008 grdn:0.179 lr:1.4e-07 updt_s:0.088 data_s:0.006
+Training:  97%|████████████████████████████████████▊ | 24200/25000 [41:43<01:15, 10.66step/s]INFO 2026-05-20 23:03:27 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.68 loss:0.007 grdn:0.171 lr:1.3e-07 updt_s:0.082 data_s:0.012
+Training:  97%|████████████████████████████████████▊ | 24220/25000 [41:45<01:18,  9.93step/s]INFO 2026-05-20 23:03:29 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.68 loss:0.007 grdn:0.168 lr:1.2e-07 updt_s:0.089 data_s:0.010
+Training:  97%|████████████████████████████████████▊ | 24240/25000 [41:47<01:12, 10.50step/s]INFO 2026-05-20 23:03:31 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.69 loss:0.008 grdn:0.182 lr:1.2e-07 updt_s:0.086 data_s:0.010
+Training:  97%|████████████████████████████████████▉ | 24260/25000 [41:49<01:14,  9.87step/s]INFO 2026-05-20 23:03:33 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.69 loss:0.007 grdn:0.173 lr:1.1e-07 updt_s:0.086 data_s:0.009
+Training:  97%|████████████████████████████████████▉ | 24279/25000 [41:50<01:08, 10.52step/s]INFO 2026-05-20 23:03:35 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.70 loss:0.008 grdn:0.185 lr:1.1e-07 updt_s:0.086 data_s:0.010
+Training:  97%|████████████████████████████████████▉ | 24300/25000 [41:52<01:06, 10.45step/s]INFO 2026-05-20 23:03:37 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.70 loss:0.007 grdn:0.178 lr:1.0e-07 updt_s:0.087 data_s:0.011
+Training:  97%|████████████████████████████████████▉ | 24320/25000 [41:54<01:08,  9.99step/s]INFO 2026-05-20 23:03:39 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.71 loss:0.007 grdn:0.159 lr:9.5e-08 updt_s:0.092 data_s:0.007
+Training:  97%|████████████████████████████████████▉ | 24340/25000 [41:56<01:01, 10.78step/s]INFO 2026-05-20 23:03:41 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.71 loss:0.007 grdn:0.191 lr:8.9e-08 updt_s:0.089 data_s:0.006
+Training:  97%|█████████████████████████████████████ | 24360/25000 [41:58<00:59, 10.81step/s]INFO 2026-05-20 23:03:43 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.72 loss:0.007 grdn:0.175 lr:8.4e-08 updt_s:0.082 data_s:0.012
+Training:  98%|█████████████████████████████████████ | 24380/25000 [42:00<01:02,  9.85step/s]INFO 2026-05-20 23:03:45 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.72 loss:0.008 grdn:0.180 lr:7.9e-08 updt_s:0.090 data_s:0.006
+Training:  98%|█████████████████████████████████████ | 24399/25000 [42:02<00:59, 10.15step/s]INFO 2026-05-20 23:03:47 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.73 loss:0.007 grdn:0.180 lr:7.4e-08 updt_s:0.087 data_s:0.006
+Training:  98%|█████████████████████████████████████ | 24419/25000 [42:04<00:56, 10.20step/s]INFO 2026-05-20 23:03:49 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.73 loss:0.007 grdn:0.172 lr:6.9e-08 updt_s:0.090 data_s:0.008
+Training:  98%|█████████████████████████████████████▏| 24439/25000 [42:06<00:56,  9.99step/s]INFO 2026-05-20 23:03:51 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.74 loss:0.007 grdn:0.179 lr:6.5e-08 updt_s:0.087 data_s:0.008
+Training:  98%|█████████████████████████████████████▏| 24460/25000 [42:08<00:52, 10.38step/s]INFO 2026-05-20 23:03:52 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.74 loss:0.007 grdn:0.168 lr:6.0e-08 updt_s:0.091 data_s:0.006
+Training:  98%|█████████████████████████████████████▏| 24480/25000 [42:10<00:51, 10.01step/s]INFO 2026-05-20 23:03:54 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.75 loss:0.007 grdn:0.175 lr:5.6e-08 updt_s:0.086 data_s:0.009
+Training:  98%|█████████████████████████████████████▏| 24500/25000 [42:12<00:46, 10.74step/s]INFO 2026-05-20 23:03:56 ot_train.py:488 step:24K smpl:2M ep:4K epch:5.75 loss:0.007 grdn:0.176 lr:5.2e-08 updt_s:0.086 data_s:0.009
+Training:  98%|█████████████████████████████████████▎| 24519/25000 [42:14<00:51,  9.43step/s]INFO 2026-05-20 23:03:58 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.75 loss:0.008 grdn:0.182 lr:4.8e-08 updt_s:0.090 data_s:0.011
+Training:  98%|█████████████████████████████████████▎| 24539/25000 [42:16<00:43, 10.61step/s]INFO 2026-05-20 23:04:00 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.76 loss:0.007 grdn:0.176 lr:4.4e-08 updt_s:0.083 data_s:0.009
+Training:  98%|█████████████████████████████████████▎| 24560/25000 [42:18<00:43, 10.12step/s]INFO 2026-05-20 23:04:02 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.76 loss:0.008 grdn:0.163 lr:4.0e-08 updt_s:0.087 data_s:0.011
+Training:  98%|█████████████████████████████████████▎| 24580/25000 [42:20<00:41, 10.22step/s]INFO 2026-05-20 23:04:04 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.77 loss:0.006 grdn:0.162 lr:3.7e-08 updt_s:0.083 data_s:0.011
+Training:  98%|█████████████████████████████████████▍| 24600/25000 [42:22<00:35, 11.18step/s]INFO 2026-05-20 23:04:06 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.77 loss:0.008 grdn:0.169 lr:3.3e-08 updt_s:0.086 data_s:0.006
+Training:  98%|█████████████████████████████████████▍| 24620/25000 [42:24<00:36, 10.39step/s]INFO 2026-05-20 23:04:08 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.78 loss:0.007 grdn:0.166 lr:3.0e-08 updt_s:0.093 data_s:0.009
+Training:  99%|█████████████████████████████████████▍| 24640/25000 [42:26<00:36,  9.82step/s]INFO 2026-05-20 23:04:10 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.78 loss:0.008 grdn:0.173 lr:2.7e-08 updt_s:0.088 data_s:0.008
+Training:  99%|█████████████████████████████████████▍| 24660/25000 [42:28<00:32, 10.34step/s]INFO 2026-05-20 23:04:12 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.79 loss:0.007 grdn:0.172 lr:2.4e-08 updt_s:0.090 data_s:0.006
+Training:  99%|█████████████████████████████████████▌| 24679/25000 [42:30<00:31, 10.08step/s]INFO 2026-05-20 23:04:14 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.79 loss:0.007 grdn:0.169 lr:2.2e-08 updt_s:0.087 data_s:0.010
+Training:  99%|█████████████████████████████████████▌| 24699/25000 [42:32<00:30, 10.03step/s]INFO 2026-05-20 23:04:16 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.80 loss:0.007 grdn:0.158 lr:1.9e-08 updt_s:0.086 data_s:0.006
+Training:  99%|█████████████████████████████████████▌| 24720/25000 [42:34<00:26, 10.37step/s]INFO 2026-05-20 23:04:18 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.80 loss:0.007 grdn:0.162 lr:1.7e-08 updt_s:0.094 data_s:0.007
+Training:  99%|█████████████████████████████████████▌| 24740/25000 [42:36<00:26,  9.96step/s]INFO 2026-05-20 23:04:20 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.81 loss:0.007 grdn:0.152 lr:1.4e-08 updt_s:0.090 data_s:0.007
+Training:  99%|█████████████████████████████████████▋| 24760/25000 [42:38<00:24,  9.85step/s]INFO 2026-05-20 23:04:22 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.81 loss:0.007 grdn:0.192 lr:1.2e-08 updt_s:0.092 data_s:0.006
+Training:  99%|█████████████████████████████████████▋| 24780/25000 [42:40<00:23,  9.51step/s]INFO 2026-05-20 23:04:24 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.82 loss:0.006 grdn:0.175 lr:1.0e-08 updt_s:0.086 data_s:0.013
+Training:  99%|█████████████████████████████████████▋| 24800/25000 [42:42<00:18, 10.54step/s]INFO 2026-05-20 23:04:26 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.82 loss:0.006 grdn:0.159 lr:8.7e-09 updt_s:0.088 data_s:0.008
+Training:  99%|█████████████████████████████████████▋| 24820/25000 [42:44<00:17, 10.05step/s]INFO 2026-05-20 23:04:28 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.82 loss:0.007 grdn:0.154 lr:7.2e-09 updt_s:0.089 data_s:0.006
+Training:  99%|█████████████████████████████████████▊| 24840/25000 [42:46<00:15, 10.22step/s]INFO 2026-05-20 23:04:30 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.83 loss:0.007 grdn:0.168 lr:5.7e-09 updt_s:0.092 data_s:0.008
+Training:  99%|█████████████████████████████████████▊| 24860/25000 [42:48<00:14,  9.91step/s]INFO 2026-05-20 23:04:32 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.83 loss:0.007 grdn:0.171 lr:4.5e-09 updt_s:0.088 data_s:0.009
+Training: 100%|█████████████████████████████████████▊| 24879/25000 [42:49<00:12,  9.95step/s]INFO 2026-05-20 23:04:34 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.84 loss:0.007 grdn:0.172 lr:3.3e-09 updt_s:0.090 data_s:0.007
+Training: 100%|█████████████████████████████████████▊| 24899/25000 [42:51<00:10,  9.72step/s]INFO 2026-05-20 23:04:36 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.84 loss:0.008 grdn:0.176 lr:2.4e-09 updt_s:0.089 data_s:0.009
+Training: 100%|█████████████████████████████████████▉| 24919/25000 [42:53<00:07, 10.15step/s]INFO 2026-05-20 23:04:38 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.85 loss:0.008 grdn:0.173 lr:1.6e-09 updt_s:0.089 data_s:0.008
+Training: 100%|█████████████████████████████████████▉| 24939/25000 [42:55<00:05, 10.72step/s]INFO 2026-05-20 23:04:40 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.85 loss:0.007 grdn:0.164 lr:9.7e-10 updt_s:0.085 data_s:0.009
+Training: 100%|█████████████████████████████████████▉| 24959/25000 [42:57<00:04,  9.96step/s]INFO 2026-05-20 23:04:42 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.86 loss:0.007 grdn:0.165 lr:4.9e-10 updt_s:0.088 data_s:0.011
+Training: 100%|█████████████████████████████████████▉| 24979/25000 [42:59<00:02, 10.20step/s]INFO 2026-05-20 23:04:44 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.86 loss:0.008 grdn:0.180 lr:1.8e-10 updt_s:0.088 data_s:0.013
+Training: 100%|█████████████████████████████████████▉| 24999/25000 [43:01<00:00, 11.13step/s]INFO 2026-05-20 23:04:46 ot_train.py:488 step:25K smpl:2M ep:4K epch:5.87 loss:0.006 grdn:0.155 lr:2.5e-11 updt_s:0.085 data_s:0.011
+INFO 2026-05-20 23:04:46 ot_train.py:502 Checkpoint policy after step 25000
+Training: 100%|██████████████████████████████████████| 25000/25000 [43:11<00:00,  9.65step/s]
+INFO 2026-05-20 23:04:55 ot_train.py:576 End of training
diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/requirements.txt b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00bf76d5340519c084ce884e472b39c2286bbb0b
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/requirements.txt
@@ -0,0 +1,113 @@
+lerobot==0.5.2
+transformers==5.5.4
+pydantic_core==2.46.4
+fsspec==2026.2.0
+MarkupSafe==3.0.3
+protobuf==7.34.1
+setuptools==80.10.2
+sentry-sdk==2.60.0
+idna==3.13
+pandas==2.3.3
+packaging==25.0
+h11==0.16.0
+nvidia-cufft-cu12==11.3.3.83
+pillow==12.2.0
+toml==0.10.2
+typing-inspect==0.9.0
+triton==3.6.0
+Jinja2==3.1.6
+networkx==3.6.1
+Farama-Notifications==0.0.6
+cloudpickle==3.1.2
+draccus==0.10.0
+pyyaml-include==1.4.1
+aiohttp==3.13.5
+markdown-it-py==4.0.0
+termcolor==3.3.0
+wandb==0.27.0
+shellingham==1.5.4
+PyYAML==6.0.3
+smmap==5.0.3
+nvidia-cusparselt-cu12==0.7.1
+charset-normalizer==3.4.7
+multiprocess==0.70.19
+tzdata==2026.2
+typer==0.25.1
+mypy_extensions==1.1.0
+multidict==6.7.1
+torch==2.10.0
+Flask==3.1.3
+nvidia-cusparse-cu12==12.5.8.93
+GitPython==3.1.50
+mdurl==0.1.2
+blinker==1.9.0
+itsdangerous==2.2.0
+datasets==4.8.5
+httpcore==1.0.9
+jsonlines==4.0.0
+nvidia-cusolver-cu12==11.7.3.90
+cmake==4.1.3
+propcache==0.5.2
+Werkzeug==3.1.8
+typing_extensions==4.15.0
+platformdirs==4.9.6
+psutil==7.2.2
+dill==0.4.1
+lerobot==0.5.2
+xxhash==3.7.0
+gymnasium==1.3.0
+nvidia-cublas-cu12==12.8.4.1
+opencv-python-headless==4.13.0.92
+mergedeep==1.3.4
+pyarrow==24.0.0
+annotated-types==0.7.0
+anyio==4.13.0
+cuda-pathfinder==1.5.4
+six==1.17.0
+nvidia-cuda-runtime-cu12==12.8.90
+click==8.3.3
+tqdm==4.67.3
+annotated-doc==0.0.4
+frozenlist==1.8.0
+av==15.1.0
+pydantic==2.13.4
+torchcodec==0.10.0
+zipp==4.1.0
+mpmath==1.3.0
+pytz==2026.2
+importlib_metadata==9.0.0
+huggingface_hub==1.13.0
+hf-xet==1.4.3
+attrs==26.1.0
+nvidia-nvjitlink-cu12==12.8.93
+sympy==1.14.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-cudnn-cu12==9.10.2.21
+diffusers==0.35.2
+yarl==1.23.0
+cuda-bindings==12.9.4
+nvidia-cuda-cupti-cu12==12.8.90
+tokenizers==0.22.2
+urllib3==2.6.3
+aiosignal==1.4.0
+torchvision==0.25.0
+httpx==0.28.1
+requests==2.33.1
+certifi==2026.4.22
+rich==15.0.0
+python-dateutil==2.9.0.post0
+nvidia-nccl-cu12==2.27.5
+accelerate==1.13.0
+numpy==2.2.6
+nvidia-nvshmem-cu12==3.4.5
+nvidia-curand-cu12==10.3.9.90
+gitdb==4.0.12
+regex==2026.5.9
+aiohappyeyeballs==2.6.1
+filelock==3.29.0
+Pygments==2.20.0
+safetensors==0.7.0
+einops==0.8.2
+nvidia-nvtx-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+typing-inspection==0.4.2
diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/wandb-metadata.json b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd4e3fc4bef38b7084a9c458e999f3f4a615b457
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/files/wandb-metadata.json
@@ -0,0 +1,69 @@
+{
+  "os": "Linux-6.8.0-90-generic-x86_64-with-glibc2.35",
+  "python": "CPython 3.12.13",
+  "startedAt": "2026-05-20T22:21:41.390284Z",
+  "args": [
+    "--policy.path=/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter1_25000steps_20260520_173844/checkpoints/022000/pretrained_model/",
+    "--dataset.repo_id=local/dagger_episodes",
+    "--dataset.root=/home/shadeform/Desktop/robot_learning/base_new_dataset_dagger_v2_676",
+    "--dataset.video_backend=pyav",
+    "--policy.device=cuda",
+    "--policy.push_to_hub=false",
+    "--policy.n_obs_steps=2",
+    "--policy.horizon=16",
+    "--policy.n_action_steps=8",
+    "--policy.vision_backbone_type=dinov2",
+    "--policy.vision_backbone=resnet18",
+    "--policy.freeze_vision_encoder=true",
+    "--policy.noise_scheduler_type=DDPM",
+    "--policy.num_train_timesteps=100",
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+    "--policy.optimizer_lr=5e-5",
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diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/logs/debug.log b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..3af46c072a77a7ccc239967a1b6c59560d16a076
--- /dev/null
+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/logs/debug.log
@@ -0,0 +1,58 @@
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Current SDK version is 0.27.0
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Configure stats pid to 3757791
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:setup_run_log_directory():723] Logging user logs to /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132/wandb/run-20260520_222141-vz6r3agr/logs/debug.log
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:setup_run_log_directory():724] Logging internal logs to /home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132/wandb/run-20260520_222141-vz6r3agr/logs/debug-internal.log
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():851] calling init triggers
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():856] wandb.init called with sweep_config: {}
+config: {'dataset': {'repo_id': 'local/dagger_episodes', 'root': '/home/shadeform/Desktop/robot_learning/base_new_dataset_dagger_v2_676', 'episodes': None, 'image_transforms': {'enable': False, 'max_num_transforms': 3, 'random_order': False, 'same_cloth_color': False, 'tfs': {'color_jitter': {'weight': 1.5, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.7, 1.3], 'contrast': [0.7, 1.3], 'saturation': [0.4, 1.6], 'hue': [-0.5, 0.5]}}, 'grayscale': {'weight': 0.5, 'type': 'RandomGrayscale', 'kwargs': {'p': 1.0}}, 'shadow': {'weight': 0.8, 'type': 'RandomShadow', 'kwargs': {'num_shadows': [1, 2], 'intensity': [0.4, 0.7], 'blur_sigma': [4.0, 20.0]}}, 'highlight': {'weight': 0.5, 'type': 'RandomHighlight', 'kwargs': {'num_highlights': [0, 1], 'intensity': [0.2, 0.5], 'sigma': [20.0, 60.0]}}, 'gamma': {'weight': 0.7, 'type': 'RandomGamma', 'kwargs': {'gamma': [0.6, 1.4]}}, 'blur': {'weight': 0.0, 'type': 'GaussianBlur', 'kwargs': {'kernel_size': [3, 3], 'sigma': [0.1, 1.5]}}, 'jpeg': {'weight': 0.0, 'type': 'RandomJPEG', 'kwargs': {'quality': [60, 95]}}, 'crop': {'weight': 0.5, 'type': 'RandomCropPreserveSize', 'kwargs': {'scale': [0.97, 1.0], 'ratio': [0.99, 1.01]}}, 'affine': {'weight': 0.0, 'type': 'RandomAffine', 'kwargs': {'degrees': [-5.0, 5.0], 'translate': [0.05, 0.05]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}}}, 'revision': None, 'use_imagenet_stats': True, 'video_backend': 'pyav', 'return_uint8': False, 'streaming': False}, 'env': None, 'policy': {'type': 'diffusion', 'n_obs_steps': 2, 'input_features': {'observation.state': {'type': <FeatureType.STATE: 'STATE'>, 'shape': [6]}, 'observation.images.front': {'type': <FeatureType.VISUAL: 'VISUAL'>, 'shape': [3, 480, 640]}}, 'output_features': {'action': {'type': <FeatureType.ACTION: 'ACTION'>, 'shape': [6]}}, 'device': 'cuda', 'use_amp': False, 'use_peft': False, 'push_to_hub': False, 'repo_id': None, 'private': None, 'tags': None, 'license': None, 'pretrained_path': '/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter1_25000steps_20260520_173844/checkpoints/022000/pretrained_model', 'horizon': 16, 'n_action_steps': 8, 'normalization_mapping': {'VISUAL': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'STATE': <NormalizationMode.MIN_MAX: 'MIN_MAX'>, 'ACTION': <NormalizationMode.MIN_MAX: 'MIN_MAX'>}, 'drop_n_last_frames': 7, 'vision_backbone_type': 'dinov2', 'vision_encoder_name': 'facebook/dinov2-small', 'freeze_vision_encoder': True, 'vision_backbone': 'resnet18', 'resize_shape': [224, 224], 'crop_ratio': 1.0, 'crop_shape': None, 'crop_is_random': True, 'pretrained_backbone_weights': 'ResNet18_Weights.IMAGENET1K_V1', 'use_group_norm': False, 'spatial_softmax_num_keypoints': 32, 'vit_pool_type': 'spatial_softmax', 'vit_feature_dim': 512, 'use_separate_rgb_encoder_per_camera': True, 'down_dims': [512, 1024, 2048], 'kernel_size': 5, 'n_groups': 8, 'diffusion_step_embed_dim': 128, 'use_film_scale_modulation': True, 'noise_scheduler_type': 'DDPM', 'num_train_timesteps': 100, 'beta_schedule': 'squaredcos_cap_v2', 'beta_start': 0.0001, 'beta_end': 0.02, 'prediction_type': 'epsilon', 'clip_sample': True, 'clip_sample_range': 1.0, 'num_inference_steps': 100, 'compile_model': False, 'compile_mode': 'reduce-overhead', 'do_mask_loss_for_padding': False, 'optimizer_lr': 5e-05, 'optimizer_betas': [0.95, 0.999], 'optimizer_eps': 1e-08, 'optimizer_weight_decay': 1e-06, 'scheduler_name': 'cosine', 'scheduler_warmup_steps': 100}, 'reward_model': None, 'output_dir': '/home/shadeform/Desktop/robot_learning/outputs/train/diffusion_dagger_iter2_25000steps_20260520_222132', 'job_name': 'diffusion_dagger_iter2_25000steps_20260520_222132', 'resume': False, 'seed': 1000, 'cudnn_deterministic': False, 'num_workers': 16, 'batch_size': 64, 'prefetch_factor': 4, 'persistent_workers': True, 'steps': 25000, 'eval_freq': 0, 'log_freq': 20, 'tolerance_s': 0.0001, 'save_checkpoint': True, 'save_freq': 2000, 'use_policy_training_preset': True, 'optimizer': {'type': 'adam', 'lr': 5e-05, 'weight_decay': 1e-06, 'grad_clip_norm': 10.0, 'betas': [0.95, 0.999], 'eps': 1e-08}, 'scheduler': {'type': 'diffuser', 'num_warmup_steps': 100, 'name': 'cosine'}, 'eval': {'n_episodes': 50, 'batch_size': 19, 'use_async_envs': True}, 'wandb': {'enable': True, 'disable_artifact': False, 'project': 'lerobot', 'entity': None, 'notes': None, 'run_id': None, 'mode': None, 'add_tags': True}, 'peft': None, 'sample_weighting': None, 'rename_map': {}, 'checkpoint_path': None, '_wandb': {}}
+2026-05-20 22:21:41,391 INFO    MainThread:3757791 [wandb_init.py:init():899] starting backend
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+2026-05-20 22:21:41,813 INFO    MainThread:3757791 [wandb_init.py:init():919] backend started and connected
+2026-05-20 22:21:41,814 INFO    MainThread:3757791 [wandb_init.py:init():989] updated telemetry
+2026-05-20 22:21:41,818 INFO    MainThread:3757791 [wandb_init.py:init():1012] communicating run to backend with 90.0 second timeout
+2026-05-20 22:21:42,107 INFO    MainThread:3757791 [wandb_init.py:init():1057] starting run threads in backend
+2026-05-20 22:21:42,160 INFO    MainThread:3757791 [wandb_run.py:_console_start():2509] atexit reg
+2026-05-20 22:21:42,161 INFO    MainThread:3757791 [wandb_run.py:_redirect():2359] redirect: wrap_raw
+2026-05-20 22:21:42,161 INFO    MainThread:3757791 [wandb_run.py:_redirect():2428] Wrapping output streams.
+2026-05-20 22:21:42,161 INFO    MainThread:3757791 [wandb_run.py:_redirect():2451] Redirects installed.
+2026-05-20 22:21:42,162 INFO    MainThread:3757791 [wandb_init.py:init():1095] run started, returning control to user process
+2026-05-20 23:04:55,815 INFO    wandb-AsyncioManager-main:3757791 [service_client.py:_forward_responses():122] Reached EOF.
+2026-05-20 23:04:55,815 INFO    wandb-AsyncioManager-main:3757791 [mailbox.py:close():154] Closing mailbox, abandoning 2 handles.
+2026-05-20 23:04:58,253 ERROR   wandb-AsyncioManager-main:3757791 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_stopping.py", line 87, in loop
+    should_stop = await self._check_should_stop()
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_stopping.py", line 115, in _check_should_stop
+    handle = await self._interface.deliver_async(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 58, in deliver_async
+    handle = await self._client.deliver(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/service/service_client.py", line 57, in deliver
+    handle = self._mailbox.require_response(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/mailbox/mailbox.py", line 89, in require_response
+    raise MailboxClosedError()
+wandb.sdk.mailbox.mailbox.MailboxClosedError
+2026-05-20 23:05:04,106 ERROR   wandb-AsyncioManager-main:3757791 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_messages.py", line 105, in loop
+    await self._poll_and_print(timeout=None)
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/run_messages.py", line 133, in _poll_and_print
+    handle = await self._interface.deliver_async(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/interface/interface_sock.py", line 58, in deliver_async
+    handle = await self._client.deliver(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/lib/service/service_client.py", line 57, in deliver
+    handle = self._mailbox.require_response(request)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/shadeform/Desktop/robot_learning/lerobot/.venv/lib/python3.12/site-packages/wandb/sdk/mailbox/mailbox.py", line 89, in require_response
+    raise MailboxClosedError()
+wandb.sdk.mailbox.mailbox.MailboxClosedError
diff --git a/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/run-vz6r3agr.wandb b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/run-vz6r3agr.wandb
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index 0000000000000000000000000000000000000000..6151add42178536118345a3b57f6a8d73b49ede6
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+++ b/checkpoints/dagger/2iter/wandb/run-20260520_222141-vz6r3agr/run-vz6r3agr.wandb
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