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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_all
+    data_root_dir: ./playground/Datasets/LEROBOT_LIBERO_DATA
+    dataset_py: lerobot_datasets
+    per_device_batch_size: 8
+    video_backend: torchvision_av
+framework:
+  action_model:
+    action_dim: 7
+    action_horizon: 8
+    action_model_type: DiT-B
+    add_pos_embed: true
+    diffusion_model_cfg:
+      cross_attention_dim: 4096
+      dropout: 0.2
+      final_dropout: true
+      interleave_self_attention: true
+      norm_type: ada_norm
+      num_layers: 16
+      output_dim: 1024
+      positional_embeddings: null
+    future_action_window_size: 7
+    hidden_size: 1024
+    max_seq_len: 1024
+    noise_beta_alpha: 1.5
+    noise_beta_beta: 1.0
+    noise_s: 0.999
+    num_inference_timesteps: 4
+    num_target_vision_tokens: 32
+    num_timestep_buckets: 1000
+    past_action_window_size: 0
+    state_dim: 7
+  name: QwenGR00T
+  qwenvl:
+    base_vlm: /mnt/18T/starVLAproject/Qwen3-VL-8B-Instruct
+output_dir: /starvla/Checkpoints/libero4in1_QwenGR00T_2node_0201_1721
+run_id: libero4in1_QwenGR00T_2node_0201_1721
+run_root_dir: /starvla/Checkpoints
+seed: 42
+trainer:
+  eval_interval: 100
+  freeze_modules: true
+  gradient_accumulation_steps: 4
+  gradient_clipping: 1.0
+  is_resume: false
+  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: 80000
+  num_warmup_steps: 5000
+  optimizer:
+    betas:
+    - 0.9
+    - 0.95
+    eps: 1.0e-08
+    weight_decay: 1.0e-08
+  save_interval: 10000
+  scheduler_specific_kwargs:
+    min_lr: 1.0e-06
+wandb_entity: xiguapi
+wandb_project: Qwen3VL_libero_all_QwenGR00T_2node