run_id: 0501_robotwin_all_qwenrealpi_all_2 run_root_dir: ./results/Checkpoints seed: 42 trackers: - jsonl - wandb wandb_entity: your_wandb_entity wandb_project: starVLA_Robotwin is_debug: false version_id: '0.21' framework: name: QwenRealPI qwenvl: base_vlm: /inspire/qb-ilm/project/qproject-fundationmodel/public/zzt/models/Qwen/Qwen3-VL-4B-Instruct attn_implementation: flash_attention_2 vl_hidden_dim: 2048 action_model: action_dim: 14 state_dim: 14 action_horizon: 16 hidden_size: 1024 num_inference_timesteps: 10 noise_beta_alpha: 1.5 noise_beta_beta: 1.0 noise_s: 0.999 init_from_llm: false future_action_window_size: 15 action_hidden_dim: 1024 past_action_window_size: 0 obs_image_size: - 224 - 224 datasets: vla_data: dataset_py: lerobot_datasets data_root_dir: playground/Datasets/RoboTwin data_mix: robotwin_all action_type: abs_qpos action_mode: abs sequential_step_sampling: false default_image_resolution: - 3 - 224 - 224 per_device_batch_size: 32 load_all_data_for_training: true obs: - image_0 image_size: - 224 - 224 video_backend: pyav trainer: epochs: 100 max_train_steps: 150000 num_warmup_steps: 5000 save_interval: 10000 eval_interval: 1000 learning_rate: base: 1.0e-05 qwen_vl_interface: 1.0e-05 action_model: 0.0001 lr_scheduler_type: cosine_with_min_lr scheduler_specific_kwargs: min_lr: 5.0e-07 freeze_modules: qwen_vl_interface.model.model.visual loss_scale: vla: 1.0 max_grad_norm: 1.0 warmup_ratio: 0.1 weight_decay: 0.0 logging_frequency: 100 gradient_clipping: 1.0 gradient_accumulation_steps: 1 optimizer: name: AdamW betas: - 0.9 - 0.95 eps: 1.0e-08 weight_decay: 1.0e-08 is_resume: false resume_epoch: null resume_step: null enable_gradient_checkpointing: true enable_mixed_precision_training: true valid_size: 256 config_yaml: ./examples/Robotwin/train_files/real_pi.yaml output_dir: ./results/Checkpoints/0501_robotwin_all_qwenrealpi_all_2