StarVLA/Qwen3-VL-PI-LIBERO-4in1

Qwen3-VL PI for LIBERO 4-in-1

This repository contains a StarVLA QwenPI checkpoint initialized from Qwen3-VL-4B-Instruct and trained on the combined LIBERO libero_10 + libero_goal + libero_object + libero_spatial mixture. The model uses Qwen3-VL features with a layer-wise cross-DiT flow-matching action head.

Model and action contract

Item	Value
Framework	StarVLA QwenPI
Base VLM	Qwen3-VL-4B-Instruct
Action head	Post-restructure layer-wise cross-DiT: 36 layers, 2,560 latent width, 40 heads; each block receives the corresponding one of the last 36 Qwen3-VL hidden layers
Action representation	7D delta_qpos
Configured state dimension	7
Action horizon	8
Camera input	One image_0 RGB view, default 224 × 224
Inference flow steps	4
Dataset mixture	libero_all
Released checkpoint	checkpoints/steps_100000_pytorch_model.pt

Training recipe

Setting	Value
Maximum and released step	100,000
VLA per-device batch	8
Auxiliary VLM per-device batch	4
Gradient accumulation	1
Warm-up	5,000 steps
Base / VLM-interface / action LR	3e-5 / 1e-5 / 1e-4
VLA / auxiliary VLM loss scale	1.0 / 0.1
Training flow repeats	2 in the matching public QwenPI implementation; YAML action-model value 8 is overridden
freeze_modules	Packaged boolean true; the public trainer expects module paths as a string, so this value names/selects no modules
Seed	42

LIBERO evaluation

The following success rates are retained from the original Hub Card.

Training Steps	libero_goal	libero_object	libero_spatial	libero_10	Average Score
10,000	0.752	0.850	0.896	0.460	0.740
20,000	0.834	0.886	0.952	0.636	0.827
30,000	0.958	0.996	0.988	0.884	0.957
40,000	0.948	0.982	0.986	0.860	0.944
50,000	0.974	0.998	0.984	0.928	0.971
60,000	0.974	0.996	0.976	0.908	0.964
70,000	0.974	0.992	0.986	0.930	0.971
80,000	0.972	0.976	0.990	0.958	0.974
90,000	0.970	0.990	0.976	0.952	0.972
100,000	0.962	0.986	0.992	0.960	0.975

Only the 100k weight is included. The repository does not include raw per-episode evaluation logs for the intermediate rows.

Download and evaluation

This released checkpoint uses the historical, non-canonical LayerwiseFM forward behavior. Use the documented compatibility override:

huggingface-cli download StarVLA/Qwen3-VL-PI-LIBERO-4in1 \
  --local-dir Qwen3-VL-PI-LIBERO-4in1

CKPT="$PWD/Qwen3-VL-PI-LIBERO-4in1/checkpoints/steps_100000_pytorch_model.pt"
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" --port 6694 --use_bf16 \
  --config_override framework.qwenvl.base_vlm=Qwen/Qwen3-VL-4B-Instruct \
  --config_override framework.action_model.diffusion_model_cfg.use_canonical_forward=false

Run the simulator separately according to StarVLA's LIBERO guide.

Evidence and limitations

• Structure, action contract, and training values were checked against config.yaml; checkpoint availability was checked against the Hub file tree.
• The YAML's dino, action_model_type: DiT-B, 16-layer, and repeat-8 fields are not the constructed post-restructure QwenPI shape. The matching public implementation derives the 36-layer, 2,560-wide head from Qwen3-VL and does not construct DINO.
• Evaluation numbers are Card-reported. The Hub repository does not package the raw rollout logs needed to independently aggregate them.
• The saved base-model and data paths are environment-local and require mapping.
• Results depend on the historical LayerwiseFM compatibility override, normalization statistics, camera/state contract, and evaluator version.
• Results cover LIBERO simulation only; real-robot and cross-embodiment performance are not established.