policy-test-2 / 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.
action_chunk_offset: 1
action_chunk_size: 16
action_dim: 14
action_mode: abs
current_image_color_jitter:
brightness: 0.3
contrast: 0.4
hue: 0.08
saturation: 0.5
data_mix: xpolicylab
data_root_dir: /mnt/project/world_model/data/sim_benchmark
dataset_py: lerobot_datasets
delete_pause_frame: false
image_only_mode: true
img_interval: 1
jitter_current_image_only: true
num_chunk: 1
per_device_batch_size: 6
state_dim: 14
stats_filename: meta/stats.json
training_task_weights:
- 1.0
training_tasks:
- policy
use_dummy_dataset: 0
val_ratio: 0.0
video_chunk_offset: 16
video_chunk_size: 1
vlm_block_condition_mode: causal_shared
framework:
action_model:
action_dim: 14
action_horizon: 16
action_model_type: DiT-L
add_pos_embed: true
diffusion_model_cfg:
cross_attention_dim: 2560
dropout: 0.0
final_dropout: false
interleave_self_attention: true
norm_type: ada_norm
num_layers: 16
output_dim: 2560
positional_embeddings: learnable
future_action_window_size: 15
future_obs_index: 16
hidden_size: 2560
logit_mean: 0.0
logit_std: 1.0
max_num_embodiments: 1
max_seq_len: 4096
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_loss_weight: 1.0
only_policy: false
only_wo_video_gen: false
past_action_window_size: 0
policy_and_video_gen: false
state_dim: 14
use_attn_mask: true
vision_encoder_path: /mnt/home/liukai/World-Action-Model/pretrained
vision_encoder_size: s
vision_encoder_type: dinov3
visual_token_fusion_mode: direct_tokens
name: QwenMMDiT
qwenvl:
base_vlm: /mnt/home/liukai/starVLA/playground/pretrained/vlm/Qwen3-VL-4B-Instruct
pad_to: 16
output_dir: /workplace/world_model/checkpoints/lda_v2/post-train/robodojo/RoboDojo-cotrain-robodojo-multiview-0
run_id: RoboDojo-cotrain-robodojo-multiview-0
run_root_dir: /workplace/world_model/checkpoints/lda_v2/post-train/robodojo
seed: 0
swanlab_mode: cloud
swanlab_project: xpolicy_lab
swanlab_workspace: Onway
trackers:
- jsonl
- swanlab
trainer:
enable_gradient_checkpointing: false
eval_interval: 5000
freeze_modules: action_model.vision_encoder
gradient_warn: 100.0
is_resume: false
learning_rate:
action_model: 0.0001
base: 4.0e-05
qwen_vl_interface: 1.0e-05
lerobot_eval:
action_horizon: 16
enabled: true
end_traj: null
max_eval_steps: 720
start_traj: 0
traj_count: 1
logging_frequency: 100
lr_scheduler_type: cosine_with_min_lr
max_checkpoints: null
max_train_steps: 160000
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
torch_compile: 0
wandb_project: LDA