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@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 checkpoints/wandb/wandb/run-20260316_073559-h1hybozy/run-h1hybozy.wandb filter=lfs diff=lfs merge=lfs -text
 checkpoints/wandb/wandb/run-20260316_085932-77uivys0/run-77uivys0.wandb filter=lfs diff=lfs merge=lfs -text
 checkpoints/pretrained_goal_2000step/wandb/wandb/run-20260316_073559-h1hybozy/run-h1hybozy.wandb filter=lfs diff=lfs merge=lfs -text
+checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085932-77uivys0/run-77uivys0.wandb filter=lfs diff=lfs merge=lfs -text

checkpoints/finetune_task2_2000step/checkpoints/steps_1000_pytorch_model.pt

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checkpoints/finetune_task2_2000step/config.yaml

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+datasets:
+  vla_data:
+    CoT_prompt: Your task is {instruction}. To identify the key objects for your task.
+      Locate their bounding boxes in [x1,y1,x2,y2] format.
+    data_mix: libero_90_task_2
+    data_root_dir: playground/Datasets/LEROBOT_LIBERO_DATA
+    dataset_py: lerobot_datasets
+    per_device_batch_size: 1
+    sequential_step_sampling: false
+    video_backend: torchvision_av
+framework:
+  action_model:
+    action_dim: 7
+    future_action_window_size: 7
+    past_action_window_size: 0
+  name: QwenFast
+  qwenvl:
+    base_vlm: playground/Pretrained_models/Qwen2.5-VL-3B-Instruct-Action
+output_dir: ./results/Checkpoints/finetune_task2_2000step
+run_id: finetune_task2_2000step
+run_root_dir: ./results/Checkpoints
+seed: 42
+trainer:
+  eval_interval: 100
+  freeze_modules: qwen_vl_interface.model.model.visual,dino_encoder
+  gradient_accumulation_steps: 1
+  gradient_clipping: 1.0
+  is_resume: true
+  learning_rate:
+    action_model: 0.0001
+    base: 2.5e-05
+    qwen_vl_interface: 1.0e-05
+  logging_frequency: 100
+  lr_scheduler_type: cosine_with_min_lr
+  max_train_steps: 2000
+  num_warmup_steps: 5000
+  optimizer:
+    betas:
+    - 0.9
+    - 0.95
+    eps: 1.0e-08
+    weight_decay: 1.0e-08
+  pretrained_checkpoint: /content/starVLA_r/results/Checkpoints/Qwen2.5-VL-FAST-LIBERO-4in1/checkpoints/steps_30000_pytorch_model.pt
+  save_interval: 500
+  scheduler_specific_kwargs:
+    min_lr: 1.0e-06
+wandb_entity: michellelin9102-usc
+wandb_project: starVLA_Libero

checkpoints/finetune_task2_2000step/dataset_statistics.json

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checkpoints/finetune_task2_2000step/final_model/pytorch_model.pt

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checkpoints/finetune_task2_2000step/summary.jsonl

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+{"steps": 1000}
+{"steps": 1500}
+{"steps": 2000}

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/files/config.yaml

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+    value:
+        cli_version: 0.24.2
+        e:
+            qhesnx8zyogcsl0ullfxd51tpupacfik:
+                args:
+                    - --config_yaml
+                    - ./examples/LIBERO/train_files/my_libero_finetune.yaml
+                codePath: starVLA/training/train_starvla.py
+                codePathLocal: starVLA/training/train_starvla.py
+                cpu_count: 6
+                cpu_count_logical: 12
+                cudaVersion: "13.0"
+                disk:
+                    /:
+                        total: "253055008768"
+                        used: "154931621888"
+                email: chihhans@usc.edu
+                executable: /usr/local/envs/starvla/bin/python3.10
+                git:
+                    commit: 87ed38d93933a6251cb05aaeaaf522ec2a4ea177
+                    remote: https://github.com/tliao730/starVLA_r
+                gpu: NVIDIA A100-SXM4-80GB
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100-SXM4-80GB
+                      uuid: GPU-1000e8c7-f9d7-74b0-8fdb-aad3f6d24e69
+                host: c89e62d63bf0
+                memory:
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+                os: Linux-6.6.113+-x86_64-with-glibc2.35
+                program: /content/starVLA_r/starVLA/training/train_starvla.py
+                python: CPython 3.10.20
+                root: ./results/Checkpoints/finetune_task2_2000step/wandb
+                startedAt: "2026-03-16T08:53:38.423184Z"
+                writerId: qhesnx8zyogcsl0ullfxd51tpupacfik
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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/files/output.log

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+03/16 [08:53:39] INFO     | >> [RANK 0] ***** Training      train_starvla.py:326
+                          Configuration *****
+                 INFO     | >> [RANK 0]   Total             train_starvla.py:327
+                          optimization steps = 2000
+                 INFO     | >> [RANK 0]   Per device batch  train_starvla.py:328
+                          size = 8
+                 INFO     | >> [RANK 0]   Gradient          train_starvla.py:329
+                          accumulation steps = 1
+                 INFO     | >> [RANK 0]   Total batch size  train_starvla.py:330
+                          = 8
+  0%|          | 0/2000 [00:00<?, ?it/s]Traceback (most recent call last):
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 427, in <module>
+    main(cfg)
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 398, in main
+    trainer.train()
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 276, in train
+    step_metrics = self._train_step(batch_vla)
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 342, in _train_step
+    self.accelerator.backward(total_loss)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/accelerator.py", line 2830, in backward
+    self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 281, in backward
+    self.engine.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2378, in step
+    self._take_model_step(lr_kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2281, in _take_model_step
+    self.optimizer.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1923, in step
+    self._optimizer_step(i)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1829, in _optimizer_step
+    self.optimizer.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper
+    return func.__get__(opt, opt.__class__)(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper
+    out = func(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
+    ret = func(self, *args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 220, in step
+    adamw(
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 154, in maybe_fallback
+    return func(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 782, in adamw
+    func(
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 606, in _multi_tensor_adamw
+    exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.51 GiB. GPU 0 has a total capacity of 79.25 GiB of which 7.76 GiB is free. Including non-PyTorch memory, this process has 71.48 GiB memory in use. Of the allocated memory 53.06 GiB is allocated by PyTorch, and 17.34 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 427, in <module>
+[rank0]:     main(cfg)
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 398, in main
+[rank0]:     trainer.train()
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 276, in train
+[rank0]:     step_metrics = self._train_step(batch_vla)
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 342, in _train_step
+[rank0]:     self.accelerator.backward(total_loss)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/accelerator.py", line 2830, in backward
+[rank0]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 281, in backward
+[rank0]:     self.engine.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2378, in step
+[rank0]:     self._take_model_step(lr_kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2281, in _take_model_step
+[rank0]:     self.optimizer.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1923, in step
+[rank0]:     self._optimizer_step(i)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1829, in _optimizer_step
+[rank0]:     self.optimizer.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper
+[rank0]:     return func.__get__(opt, opt.__class__)(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper
+[rank0]:     out = func(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
+[rank0]:     ret = func(self, *args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 220, in step
+[rank0]:     adamw(
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 154, in maybe_fallback
+[rank0]:     return func(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 782, in adamw
+[rank0]:     func(
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 606, in _multi_tensor_adamw
+[rank0]:     exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.51 GiB. GPU 0 has a total capacity of 79.25 GiB of which 7.76 GiB is free. Including non-PyTorch memory, this process has 71.48 GiB memory in use. Of the allocated memory 53.06 GiB is allocated by PyTorch, and 17.34 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/files/requirements.txt

@@ -0,0 +1,190 @@
+starVLA==1.0.1
+grpcio==1.78.0
+albucore==0.0.17
+qwen-vl-utils==0.0.14
+GitPython==3.1.46
+huggingface-hub==0.35.3
+transformers-stream-generator==0.0.4
+httpcore==1.0.9
+einops==0.8.2
+mpmath==1.3.0
+accelerate==1.13.0
+nvidia-cusparselt-cu12==0.7.1
+psutil==7.2.2
+tabulate==0.10.0
+nvidia-cudnn-cu12==9.1.0.70
+safetensors==0.7.0
+draccus==0.10.0
+typing_extensions==4.15.0
+xxhash==3.6.0
+nvidia-nccl-cu12==2.21.5
+hf-xet==1.4.2
+python-dateutil==2.9.0.post0
+wheel==0.46.3
+propcache==0.4.1
+orderly-set==5.5.0
+Werkzeug==3.1.6
+hjson==3.1.0
+sentry-sdk==2.54.0
+yarl==1.23.0
+frozenlist==1.8.0
+nvidia-nvjitlink-cu12==12.4.127
+click==8.3.1
+multidict==6.7.1
+tifffile==2025.5.10
+rerun-sdk==0.26.2
+pydantic_core==2.41.5
+websocket==0.2.1
+zope.event==6.1
+nvidia-cusolver-cu12==11.6.1.9
+pandas==2.3.3
+cloudpickle==3.1.2
+greenlet==3.3.2
+pyserial==3.5
+packaging==25.0
+antlr4-python3-runtime==4.9.3
+nvidia-cufile-cu12==1.13.1.3
+nvidia-cublas-cu12==12.4.5.8
+py-cpuinfo==9.0.0
+typeguard==4.5.1
+pytz==2026.1.post1
+PyYAML==6.0.3
+pillow==12.1.1
+requests==2.32.5
+prompt_toolkit==3.0.52
+setuptools==80.10.2
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+torch==2.5.1+cu124
+markdown-it-py==4.0.0
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+starVLA==1.0.1
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+backports.tarfile==1.2.0
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+jaraco.text==4.0.0
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+more-itertools==10.8.0
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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/files/wandb-metadata.json

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+  "python": "CPython 3.10.20",
+  "startedAt": "2026-03-16T08:53:38.423184Z",
+  "args": [
+    "--config_yaml",
+    "./examples/LIBERO/train_files/my_libero_finetune.yaml"
+  ],
+  "program": "/content/starVLA_r/starVLA/training/train_starvla.py",
+  "codePath": "starVLA/training/train_starvla.py",
+  "codePathLocal": "starVLA/training/train_starvla.py",
+  "git": {
+    "remote": "https://github.com/tliao730/starVLA_r",
+    "commit": "87ed38d93933a6251cb05aaeaaf522ec2a4ea177"
+  },
+  "email": "chihhans@usc.edu",
+  "root": "./results/Checkpoints/finetune_task2_2000step/wandb",
+  "host": "c89e62d63bf0",
+  "executable": "/usr/local/envs/starvla/bin/python3.10",
+  "cpu_count": 6,
+  "cpu_count_logical": 12,
+  "gpu": "NVIDIA A100-SXM4-80GB",
+  "gpu_count": 1,
+  "disk": {
+    "/": {
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+    }
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+  },
+  "gpu_nvidia": [
+    {
+      "name": "NVIDIA A100-SXM4-80GB",
+      "memoryTotal": "85899345920",
+      "cudaCores": 6912,
+      "architecture": "Ampere",
+      "uuid": "GPU-1000e8c7-f9d7-74b0-8fdb-aad3f6d24e69"
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+}

checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/files/wandb-summary.json

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/logs/debug-core.log

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085338-2e1zogxz/logs/debug-internal.log

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085601-uva2jmul/files/config.yaml

@@ -0,0 +1,71 @@
+_wandb:
+    value:
+        cli_version: 0.24.2
+        e:
+            gk8ouy7b5goxzi0prxrjhubz6fwv6x4w:
+                args:
+                    - --config_yaml
+                    - ./examples/LIBERO/train_files/my_libero_finetune.yaml
+                    - --datasets.vla_data.data_mix
+                    - libero_90_task_2
+                    - --run_id
+                    - finetune_task2_2000step
+                codePath: starVLA/training/train_starvla.py
+                codePathLocal: starVLA/training/train_starvla.py
+                cpu_count: 6
+                cpu_count_logical: 12
+                cudaVersion: "13.0"
+                disk:
+                    /:
+                        total: "253055008768"
+                        used: "154931699712"
+                email: chihhans@usc.edu
+                executable: /usr/local/envs/starvla/bin/python3.10
+                git:
+                    commit: 87ed38d93933a6251cb05aaeaaf522ec2a4ea177
+                    remote: https://github.com/tliao730/starVLA_r
+                gpu: NVIDIA A100-SXM4-80GB
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100-SXM4-80GB
+                      uuid: GPU-1000e8c7-f9d7-74b0-8fdb-aad3f6d24e69
+                host: c89e62d63bf0
+                memory:
+                    total: "179370471424"
+                os: Linux-6.6.113+-x86_64-with-glibc2.35
+                program: /content/starVLA_r/starVLA/training/train_starvla.py
+                python: CPython 3.10.20
+                root: ./results/Checkpoints/finetune_task2_2000step/wandb
+                startedAt: "2026-03-16T08:56:01.492879Z"
+                writerId: gk8ouy7b5goxzi0prxrjhubz6fwv6x4w
+        m: []
+        python_version: 3.10.20
+        t:
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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085601-uva2jmul/files/output.log

@@ -0,0 +1,82 @@
+03/16 [08:56:02] INFO     | >> [RANK 0] ***** Training      train_starvla.py:326
+                          Configuration *****
+                 INFO     | >> [RANK 0]   Total             train_starvla.py:327
+                          optimization steps = 2000
+                 INFO     | >> [RANK 0]   Per device batch  train_starvla.py:328
+                          size = 8
+                 INFO     | >> [RANK 0]   Gradient          train_starvla.py:329
+                          accumulation steps = 1
+                 INFO     | >> [RANK 0]   Total batch size  train_starvla.py:330
+                          = 8
+  0%|          | 0/2000 [00:00<?, ?it/s]Traceback (most recent call last):
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 427, in <module>
+    main(cfg)
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 398, in main
+    trainer.train()
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 276, in train
+    step_metrics = self._train_step(batch_vla)
+  File "/content/starVLA_r/starVLA/training/train_starvla.py", line 342, in _train_step
+    self.accelerator.backward(total_loss)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/accelerator.py", line 2830, in backward
+    self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 281, in backward
+    self.engine.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2378, in step
+    self._take_model_step(lr_kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2281, in _take_model_step
+    self.optimizer.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1923, in step
+    self._optimizer_step(i)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1829, in _optimizer_step
+    self.optimizer.step()
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper
+    return func.__get__(opt, opt.__class__)(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper
+    out = func(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
+    ret = func(self, *args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 220, in step
+    adamw(
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 154, in maybe_fallback
+    return func(*args, **kwargs)
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 782, in adamw
+    func(
+  File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 606, in _multi_tensor_adamw
+    exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.51 GiB. GPU 0 has a total capacity of 79.25 GiB of which 7.76 GiB is free. Including non-PyTorch memory, this process has 71.48 GiB memory in use. Of the allocated memory 53.06 GiB is allocated by PyTorch, and 17.34 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 427, in <module>
+[rank0]:     main(cfg)
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 398, in main
+[rank0]:     trainer.train()
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 276, in train
+[rank0]:     step_metrics = self._train_step(batch_vla)
+[rank0]:   File "/content/starVLA_r/starVLA/training/train_starvla.py", line 342, in _train_step
+[rank0]:     self.accelerator.backward(total_loss)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/accelerator.py", line 2830, in backward
+[rank0]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 281, in backward
+[rank0]:     self.engine.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2378, in step
+[rank0]:     self._take_model_step(lr_kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2281, in _take_model_step
+[rank0]:     self.optimizer.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1923, in step
+[rank0]:     self._optimizer_step(i)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1829, in _optimizer_step
+[rank0]:     self.optimizer.step()
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper
+[rank0]:     return func.__get__(opt, opt.__class__)(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper
+[rank0]:     out = func(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
+[rank0]:     ret = func(self, *args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 220, in step
+[rank0]:     adamw(
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/optimizer.py", line 154, in maybe_fallback
+[rank0]:     return func(*args, **kwargs)
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 782, in adamw
+[rank0]:     func(
+[rank0]:   File "/usr/local/envs/starvla/lib/python3.10/site-packages/torch/optim/adamw.py", line 606, in _multi_tensor_adamw
+[rank0]:     exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.51 GiB. GPU 0 has a total capacity of 79.25 GiB of which 7.76 GiB is free. Including non-PyTorch memory, this process has 71.48 GiB memory in use. Of the allocated memory 53.06 GiB is allocated by PyTorch, and 17.34 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085601-uva2jmul/files/requirements.txt

@@ -0,0 +1,190 @@
+starVLA==1.0.1
+grpcio==1.78.0
+albucore==0.0.17
+qwen-vl-utils==0.0.14
+GitPython==3.1.46
+huggingface-hub==0.35.3
+transformers-stream-generator==0.0.4
+httpcore==1.0.9
+einops==0.8.2
+mpmath==1.3.0
+accelerate==1.13.0
+nvidia-cusparselt-cu12==0.7.1
+psutil==7.2.2
+tabulate==0.10.0
+nvidia-cudnn-cu12==9.1.0.70
+safetensors==0.7.0
+draccus==0.10.0
+typing_extensions==4.15.0
+xxhash==3.6.0
+nvidia-nccl-cu12==2.21.5
+hf-xet==1.4.2
+python-dateutil==2.9.0.post0
+wheel==0.46.3
+propcache==0.4.1
+orderly-set==5.5.0
+Werkzeug==3.1.6
+hjson==3.1.0
+sentry-sdk==2.54.0
+yarl==1.23.0
+frozenlist==1.8.0
+nvidia-nvjitlink-cu12==12.4.127
+click==8.3.1
+multidict==6.7.1
+tifffile==2025.5.10
+rerun-sdk==0.26.2
+pydantic_core==2.41.5
+websocket==0.2.1
+zope.event==6.1
+nvidia-cusolver-cu12==11.6.1.9
+pandas==2.3.3
+cloudpickle==3.1.2
+greenlet==3.3.2
+pyserial==3.5
+packaging==25.0
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+    "./examples/LIBERO/train_files/my_libero_finetune.yaml",
+    "--datasets.vla_data.data_mix",
+    "libero_90_task_2",
+    "--run_id",
+    "finetune_task2_2000step"
+  ],
+  "program": "/content/starVLA_r/starVLA/training/train_starvla.py",
+  "codePath": "starVLA/training/train_starvla.py",
+  "codePathLocal": "starVLA/training/train_starvla.py",
+  "git": {
+    "remote": "https://github.com/tliao730/starVLA_r",
+    "commit": "87ed38d93933a6251cb05aaeaaf522ec2a4ea177"
+  },
+  "email": "chihhans@usc.edu",
+  "root": "./results/Checkpoints/finetune_task2_2000step/wandb",
+  "host": "c89e62d63bf0",
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+  "gpu": "NVIDIA A100-SXM4-80GB",
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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085601-uva2jmul/files/wandb-summary.json

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085601-uva2jmul/logs/debug-internal.log

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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085932-77uivys0/files/config.yaml

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+_wandb:
+    value:
+        cli_version: 0.24.2
+        e:
+            za7ksrd6rpyj9bgbyb45njmuldbqk9md:
+                args:
+                    - --config_yaml
+                    - ./examples/LIBERO/train_files/my_libero_finetune.yaml
+                    - --datasets.vla_data.data_mix
+                    - libero_90_task_2
+                    - --run_id
+                    - finetune_task2_2000step
+                codePath: starVLA/training/train_starvla.py
+                codePathLocal: starVLA/training/train_starvla.py
+                cpu_count: 6
+                cpu_count_logical: 12
+                cudaVersion: "13.0"
+                disk:
+                    /:
+                        total: "253055008768"
+                        used: "154931830784"
+                email: chihhans@usc.edu
+                executable: /usr/local/envs/starvla/bin/python3.10
+                git:
+                    commit: e952c81219e9fac2c3183a27cd378e592c4c9ef0
+                    remote: https://github.com/tliao730/starVLA_r
+                gpu: NVIDIA A100-SXM4-80GB
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100-SXM4-80GB
+                      uuid: GPU-1000e8c7-f9d7-74b0-8fdb-aad3f6d24e69
+                host: c89e62d63bf0
+                memory:
+                    total: "179370471424"
+                os: Linux-6.6.113+-x86_64-with-glibc2.35
+                program: /content/starVLA_r/starVLA/training/train_starvla.py
+                python: CPython 3.10.20
+                root: ./results/Checkpoints/finetune_task2_2000step/wandb
+                startedAt: "2026-03-16T08:59:32.301111Z"
+                writerId: za7ksrd6rpyj9bgbyb45njmuldbqk9md
+        m: []
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checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085932-77uivys0/files/output.log

@@ -0,0 +1,232 @@
+03/16 [08:59:33] INFO     | >> [RANK 0] ***** Training      train_starvla.py:326
+                          Configuration *****
+                 INFO     | >> [RANK 0]   Total             train_starvla.py:327
+                          optimization steps = 2000
+                 INFO     | >> [RANK 0]   Per device batch  train_starvla.py:328
+                          size = 1
+                 INFO     | >> [RANK 0]   Gradient          train_starvla.py:329
+                          accumulation steps = 1
+                 INFO     | >> [RANK 0]   Total batch size  train_starvla.py:330
+                          = 1
+ 20%|██        | 400/2000 [04:14<16:37,  1.60it/s, data_times=0.001, model_times=0.622]
+03/16 [09:00:38] INFO     | >> [RANK 0] Step 100, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          3.405686378479004, 'mse_score':
+                          np.float64(0.03787689109000775),
+                          'data_time':
+                          0.0012899310004286235,
+                          'model_time': 0.6190818740014947,
+                          'learning_rate':
+                          2.0000000000000002e-07, 'epoch':
+                          0.01})
+03/16 [09:01:42] INFO     | >> [RANK 0] Step 200, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          6.985860824584961, 'mse_score':
+                          np.float64(0.019201022100839522),
+                          'data_time':
+                          0.0013108330003888113,
+                          'model_time': 0.6185020459997759,
+                          'learning_rate':
+                          4.0000000000000003e-07, 'epoch':
+                          0.03})
+03/16 [09:02:45] INFO     | >> [RANK 0] Step 300, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          4.435201644897461, 'mse_score':
+                          np.float64(0.03545460837097664),
+                          'data_time':
+                          0.0013421469993772916,
+                          'model_time': 0.6221568159999151,
+                          'learning_rate':
+                          6.000000000000001e-07, 'epoch':
+                          0.04})
+03/16 [09:03:48] INFO     | >> [RANK 0] Step 400, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          3.5510754585266113, 'mse_score':
+                          np.float64(0.03448031431878018),
+                          'data_time':
+                          0.001291020000280696,
+                          'model_time': 0.621782014000928,
+                          'learning_rate':
+                          8.000000000000001e-07, 'epoch':
+                          0.05})
+03/16 [09:04:52] INFO     | >> [RANK 0] Step 500, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          3.0524678230285645, 'mse_score':
+                          np.float64(0.01984373156253733),
+                          'data_time':
+                          0.0012660100001085084,
+                          'model_time': 0.6225941900011094,
+                          'learning_rate':
+                          1.0000000000000002e-06, 'epoch':
+                          0.07})
+✅ Checkpoint saved at ./results/Checkpoints/finetune_task2_2000step/checkpoints/steps_500
+03/16 [09:05:12] INFO     | >> [RANK 0] 📊 Saving accessed  train_starvla.py:229
+                          configuration...
+                 INFO     | >> [RANK 0] ✅ Configuration    train_starvla.py:232
+                          files saved
+03/16 [09:06:17] INFO     | >> [RANK 0] Step 600, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          2.361416816711426, 'mse_score':
+                          np.float64(0.07994951865121447),
+                          'data_time':
+                          0.0011908509986824356,
+                          'model_time': 0.6221990109988838,
+                          'learning_rate':
+                          1.2000000000000002e-06, 'epoch':
+                          0.08})
+03/16 [09:07:20] INFO     | >> [RANK 0] Step 700, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          3.157254695892334, 'mse_score':
+                          np.float64(0.02363403445224792),
+                          'data_time':
+                          0.0011656720016617328,
+                          'model_time': 0.6194141920004768,
+                          'learning_rate':
+                          1.4000000000000001e-06, 'epoch':
+                          0.09})
+03/16 [09:08:24] INFO     | >> [RANK 0] Step 800, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          2.4754555225372314, 'mse_score':
+                          np.float64(0.024164106019509236),
+                          'data_time':
+                          0.0011982189989794279,
+                          'model_time': 0.6185128799988888,
+                          'learning_rate':
+                          1.6000000000000001e-06, 'epoch':
+                          0.11})
+03/16 [09:09:27] INFO     | >> [RANK 0] Step 900, Loss:     train_starvla.py:242
+                          {'action_dit_loss':
+                          2.317312479019165, 'mse_score':
+                          np.float64(0.03261401731713457),
+                          'data_time':
+                          0.0014718790007464122,
+                          'model_time': 0.6257557920016552,
+                          'learning_rate':
+                          1.8000000000000001e-06, 'epoch':
+                          0.12})
+03/16 [09:10:31] INFO     | >> [RANK 0] Step 1000, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          2.4493601322174072, 'mse_score':
+                          np.float64(0.006865942006156047),
+                          'data_time':
+                          0.0013148300004104385,
+                          'model_time': 0.6357974309994461,
+                          'learning_rate':
+                          2.0000000000000003e-06, 'epoch':
+                          0.13})
+✅ Checkpoint saved at ./results/Checkpoints/finetune_task2_2000step/checkpoints/steps_1000
+03/16 [09:10:54] INFO     | >> [RANK 0] 📊 Saving accessed  train_starvla.py:229
+                          configuration...
+                 INFO     | >> [RANK 0] ✅ Configuration    train_starvla.py:232
+                          files saved
+03/16 [09:11:58] INFO     | >> [RANK 0] Step 1100, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          2.583967924118042, 'mse_score':
+                          np.float64(0.013530660298547197),
+                          'data_time':
+                          0.0012801389984815614,
+                          'model_time': 0.6250527339998371,
+                          'learning_rate': 2.2e-06,
+                          'epoch': 0.15})
+03/16 [09:13:01] INFO     | >> [RANK 0] Step 1200, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          2.242161750793457, 'mse_score':
+                          np.float64(0.03140265184458961),
+                          'data_time':
+                          0.001347944000372081,
+                          'model_time': 0.6251432129993191,
+                          'learning_rate':
+                          2.4000000000000003e-06, 'epoch':
+                          0.16})
+03/16 [09:14:04] INFO     | >> [RANK 0] Step 1300, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          3.1264946460723877, 'mse_score':
+                          np.float64(0.016007183271521164),
+                          'data_time':
+                          0.0012498349988163682,
+                          'model_time': 0.6225897690001148,
+                          'learning_rate': 2.6e-06,
+                          'epoch': 0.18})
+03/16 [09:15:08] INFO     | >> [RANK 0] Step 1400, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          3.803471565246582, 'mse_score':
+                          np.float64(0.02869653703583849),
+                          'data_time':
+                          0.0011686699999700068,
+                          'model_time': 0.6347496790003788,
+                          'learning_rate':
+                          2.8000000000000003e-06, 'epoch':
+                          0.19})
+03/16 [09:16:12] INFO     | >> [RANK 0] Step 1500, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          1.8588244915008545, 'mse_score':
+                          np.float64(0.03212772029114899),
+                          'data_time':
+                          0.0013028009998379275,
+                          'model_time': 0.6261796300004789,
+                          'learning_rate': 3e-06, 'epoch':
+                          0.2})
+✅ Checkpoint saved at ./results/Checkpoints/finetune_task2_2000step/checkpoints/steps_1500
+03/16 [09:16:33] INFO     | >> [RANK 0] 📊 Saving accessed  train_starvla.py:229
+                          configuration...
+                 INFO     | >> [RANK 0] ✅ Configuration    train_starvla.py:232
+                          files saved
+03/16 [09:17:37] INFO     | >> [RANK 0] Step 1600, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          2.5544915199279785, 'mse_score':
+                          np.float64(0.012916433382493445),
+                          'data_time':
+                          0.0012173710001661675,
+                          'model_time': 0.6377041549985734,
+                          'learning_rate':
+                          3.2000000000000003e-06, 'epoch':
+                          0.22})
+03/16 [09:18:41] INFO     | >> [RANK 0] Step 1700, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          1.5766677856445312, 'mse_score':
+                          np.float64(0.03842659225433941),
+                          'data_time':
+                          0.001287643000978278,
+                          'model_time': 0.6260090729992953,
+                          'learning_rate':
+                          3.4000000000000005e-06, 'epoch':
+                          0.23})
+03/16 [09:19:44] INFO     | >> [RANK 0] Step 1800, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          1.4337354898452759, 'mse_score':
+                          np.float64(0.010007164092706166),
+                          'data_time':
+                          0.0012034060000587488,
+                          'model_time': 0.6258130280002661,
+                          'learning_rate':
+                          3.6000000000000003e-06, 'epoch':
+                          0.24})
+03/16 [09:20:48] INFO     | >> [RANK 0] Step 1900, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          1.7009283304214478, 'mse_score':
+                          np.float64(0.028171768109201713),
+                          'data_time':
+                          0.0013162579998606816,
+                          'model_time': 0.6234212630006368,
+                          'learning_rate':
+                          3.8000000000000005e-06, 'epoch':
+                          0.26})
+03/16 [09:21:52] INFO     | >> [RANK 0] Step 2000, Loss:    train_starvla.py:242
+                          {'action_dit_loss':
+                          1.59576416015625, 'mse_score':
+                          np.float64(0.024974743029601894),
+                          'data_time':
+                          0.0011823320000985404,
+                          'model_time': 0.6259748869997566,
+                          'learning_rate':
+                          4.000000000000001e-06, 'epoch':
+                          0.27})
+✅ Checkpoint saved at ./results/Checkpoints/finetune_task2_2000step/checkpoints/steps_2000
+03/16 [09:22:13] INFO     | >> [RANK 0] 📊 Saving accessed  train_starvla.py:229
+                          configuration...
+                 INFO     | >> [RANK 0] ✅ Configuration    train_starvla.py:232
+                          files saved
+03/16 [09:22:34] INFO     | >> [RANK 0] Training complete.  train_starvla.py:369
+                          Final model saved at
+                          ./results/Checkpoints/finetune_ta
+                          sk2_2000step/final_model

checkpoints/finetune_task2_2000step/wandb/wandb/run-20260316_085932-77uivys0/files/requirements.txt

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+  "git": {
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+    "commit": "e952c81219e9fac2c3183a27cd378e592c4c9ef0"
+  },
+  "email": "chihhans@usc.edu",
+  "root": "./results/Checkpoints/finetune_task2_2000step/wandb",
+  "host": "c89e62d63bf0",
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