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

Qwen3-VL OFT for LIBERO 4-in-1

This repository contains a StarVLA QwenOFT checkpoint initialized from Qwen3-VL-4B-Instruct and trained on the combined LIBERO libero_10 + libero_goal + libero_object + libero_spatial mixture. QwenOFT uses action placeholder tokens in the VLM sequence and an MLP/L1 regression head to predict a continuous action chunk.

Model and action contract

Item	Value
Framework	StarVLA QwenOFT
Base VLM	Qwen3-VL-4B-Instruct
Action head	Two-block residual MLP over VLM action-token features, 2,560 input / 5,120 hidden / 7 output; direct L1 regression
Action dimension	7
Action horizon	8, derived from the saved 7-step future window plus current step
Dataset mixture	libero_all
Released checkpoint	checkpoints/steps_50000_pytorch_model.pt
Checkpoint format	Complete StarVLA framework state dict (.pt)

The compact saved config does not record an explicit camera list, image resolution, or normalization mode. The checkpoint-era QwenOFT path does not feed a numeric state vector to the MLP head; later code can optionally serialize state into the instruction, which is a different prompt contract. Keep dataset_statistics.json beside the checkpoint and verify the remaining inputs in the evaluator before comparing results.

Training recipe

Setting	Value
Configured maximum steps	80,000
Released step	50,000
Per-device batch size	16
Gradient accumulation	1
Warm-up	5,000 steps
Base / VLM-interface / action LR	2.5e-5 / 1e-5 / 1e-4
Scheduler	Cosine with minimum LR 1e-6
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.

Steps	libero_object	libero_spatial	libero_goal	libero_10	Avg
20000	0.970	0.984	0.970	0.874	0.9495
30000	0.994	0.982	0.970	0.916	0.9655
40000	0.998	0.990	0.968	0.928	0.9710
50000	0.996	0.990	0.986	0.948	0.9800

Only the 50k weight is released. The repository does not include the raw per-episode evaluation logs, so the table is a reported result rather than a result independently reconstructable from this Hub repository.

Download and evaluation

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

CKPT="$PWD/Qwen3-VL-OFT-LIBERO-4in1/checkpoints/steps_50000_pytorch_model.pt"
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" --port 6694 --use_bf16 \
  --config_override framework.qwenvl.base_vlm=Qwen/Qwen3-VL-4B-Instruct

Use the separate LIBERO environment and evaluator documented in StarVLA's LIBERO guide.

Evidence and limitations

• Model structure and training settings were checked against config.yaml; the released step was checked against the Hub file tree.
• The saved base_vlm and dataset paths are environment-local and must be mapped to public/local equivalents.
• Changing normalization, camera order, state inclusion, action convention, or execution horizon can materially change success rates.
• Results are limited to LIBERO simulation. No real-robot or cross-embodiment validation is included.
• This is not a standalone Transformers from_pretrained() directory.