run_id: starvla_qwen3oft_fourier_gr1_unified_1000_franka_epfrac_1_3 run_root_dir: ./playground/Checkpoints seed: 42 wandb_entity: zwanggk wandb_project: sq_robocasa is_debug: false version_id: '0.21' framework: name: QwenOFT qwenvl: base_vlm: /gpfs/wangzixuan/shared_project/sq/5-1/starVLA/playground/Pretrained_models/Qwen3-VL-4B-Instruct-Action attn_implementation: flash_attention_2 vl_hidden_dim: 2048 action_model: action_model_type: DiT-B action_dim: 29 action_hidden_dim: 2560 future_action_window_size: 15 past_action_window_size: 0 hidden_size: 2560 add_pos_embed: true max_seq_len: 1024 state_dim: 58 action_horizon: 16 repeated_diffusion_steps: 8 noise_beta_alpha: 1.5 noise_beta_beta: 1.0 noise_s: 0.999 num_timestep_buckets: 1000 num_inference_timesteps: 4 num_target_vision_tokens: 32 diffusion_model_cfg: dropout: 0.2 final_dropout: true interleave_self_attention: true norm_type: ada_norm num_layers: 16 positional_embeddings: null output_dim: 2560 cross_attention_dim: 2048 dino: dino_backbone: dinov2_vits14 datasets: vlm_data: dataset_py: vlm_datasets dataformat: llava_json dataset_use: asv2_conversation_en,asv2_detailed_description_en,asv2_region_captioning_en,coco_internvl_longcap_en,coco_karpathy_train_567_en,coco_negative_gpt4o_en,coco_poetry_zh,coco_rem_en_zh,cocorem_exist_yorn_en,cocotextv2_en,cocotextv2_gpt4o_en,okvqa_en,refcoco_grounding_aug_en,refcoco_grounding_en,tallyqa_coco_en,toloka_grounding_aug_en,vqav2_en,vsr_en eval_dataset: aokvqa_cauldron_llava_format data_flatten: false base_interval: 2 max_pixels: 50176 min_pixels: 784 model_max_length: 2048 model_type: qwen2.5vl per_device_batch_size: 4 vla_data: dataset_py: lerobot_datasets include_state: false data_root_dir: /gpfs/wangzixuan/visual_prompting/starVLA_robocasa/playground/Datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim data_mix: fourier_gr1_unified_1000 action_type: delta_ee sequential_step_sampling: false CoT_prompt: Your task is {instruction}. To identify the key objects for your task. Locate their bounding boxes in [x1,y1,x2,y2] format. per_device_batch_size: 32 load_all_data_for_training: true obs_image_size: - 224 - 224 delete_pause_frame: false episode_subset_fraction: 0.3333333333 episode_subset_seed: 42 trainer: max_train_steps: 100000 num_warmup_steps: 5000 save_interval: 10000 eval_interval: 100 learning_rate: base: 3.0e-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: true loss_scale: vla: 1.0 vlm: 0.1 max_grad_norm: 1.0 weight_decay: 0.0 logging_frequency: 10 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 reload_modules: qwen_vl_interface pretrained_checkpoint: /gpfs/wangzixuan/shared_project/sq/5-1/starVLA/playground/Pretrained_models/0426_droid_pretrain_qwen3GR00T_triple_oft_pi_50k_16BS/final_model/pytorch_model.pt config_yaml: ./examples/Robocasa_tabletop/train_files/starvla_cotrain_robocasa_gr1.yaml output_dir: ./playground/Checkpoints/starvla_qwen3oft_fourier_gr1_unified_1000_franka_epfrac_1_3