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: xpolicylab
    data_root_dir: /mnt/project/world_model/data/sim_benchmark
    dataset_py: lerobot_datasets
    delete_pause_frame: false
    image_size:
    - 224
    - 224
    per_device_batch_size: 6
    training_task_weights:
    - 1
    - 1
    - 1
    - 1
    use_delta_action: false
framework:
  action_model:
    action_dim: 14
    action_horizon: 16
    action_model_type: DiT-B
    add_pos_embed: true
    diffusion_model_cfg:
      cross_attention_dim: 2560
      dropout: 0.2
      final_dropout: true
      interleave_self_attention: true
      norm_type: ada_norm
      num_layers: 8
      output_dim: 2560
      positional_embeddings: null
    future_action_window_size: 15
    future_obs_index: 5
    hidden_size: 2560
    max_num_embodiments: 1
    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
    num_views: 1
    obs_horizon: 2
    obs_loss_weight: 1.0
    only_policy: true
    only_wo_video_gen: false
    past_action_window_size: 0
    policy_and_video_gen: false
    state_dim: 14
    vision_encoder_path: /mnt/home/liukai/World-Action-Model/pretrained
    vision_encoder_size: s
    vision_encoder_type: dinov3
  name: QwenMMDiT
  qwenvl:
    base_vlm: /mnt/home/liukai/starVLA/playground/pretrained/vlm/Qwen3-VL-4B-Instruct
output_dir: /mnt/project/world_model/checkpoints/lda/post-train/xpolicylab/single_view
run_id: single_view
run_root_dir: /mnt/project/world_model/checkpoints/lda/post-train/xpolicylab
seed: 42
trainer:
  eval_interval: 1000
  freeze_modules: action_model.vision_encoder
  gradient_accumulation_steps: 1
  gradient_clipping: 1.0
  is_resume: false
  learning_rate:
    action_model: 0.0001
    base: 4.0e-05
    qwen_vl_interface: 1.0e-05
  logging_frequency: 100
  lr_scheduler_type: cosine_with_min_lr
  max_train_steps: 200000
  num_warmup_steps: 5000
  optimizer:
    betas:
    - 0.9
    - 0.95
    eps: 1.0e-08
    weight_decay: 1.0e-08
  pretrained_checkpoint: null
  repeated_diffusion_steps: 2
  save_interval: 5000
  scheduler_specific_kwargs:
    min_lr: 5.0e-07
wandb_entity: KaiLiu-Personal
wandb_project: lda-xpolicylab