Qwen3-VL-OFT-Robotwin2

Qwen3-VL-4B OFT for RoboTwin 2.0 Base (40K)

This repository contains a StarVLA QwenOFT checkpoint trained on the RoboTwin 2.0 Base mixture: 50 clean demonstrations for each of 50 tasks. It is a complete StarVLA state dictionary rather than a Transformers model package.

Checkpoint identity

The run history reaches 100K, but only the 40K weights are published here.

Model and control contract

Preserve the RoboTwin adapter's action reordering when converting the model's 14-D output to simulator control. A numerically correct tensor with a different joint order is not an equivalent policy input or output.

Training data and settings

dataset_statistics.json records 2,500 trajectories and 549,787 transitions for new_embodiment, matching 50 tasks x 50 clean demonstrations.

Repository-reported result boundary

The StarVLA RoboTwin README links this repository and reports 50.38% Easy success for its Qwen3-VL-4B Base column. The page does not identify the evaluated save step and this repository contains no raw evaluation logs. Therefore, 50.38% is useful repository-level context but is not verified as a score of the released 40K file. A Hard/Random score for this artifact is missing.

Do not substitute the 88.18% Easy / 88.32% Hard result: that belongs to the separate Data Scaling checkpoint trained with an additional 500 randomized demonstrations per task.

Download and load

hf download StarVLA/Qwen3-VL-OFT-Robotwin2 \
  --local-dir playground/Pretrained_models/Qwen3-VL-OFT-Robotwin2

export CKPT=playground/Pretrained_models/Qwen3-VL-OFT-Robotwin2/checkpoints/steps_40000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --config_override framework.qwenvl.base_vlm=Qwen/Qwen3-VL-4B-Instruct \
  --port 57700 \
  --use_bf16

Keep the run directory intact so StarVLA can find its configuration and statistics. Use the matching RoboTwin adapter and new_embodiment key.

Intended use and limitations

This release targets the RoboTwin 2.0 Base simulation embodiment. Results do not establish performance for the randomized Data Scaling setting, alternative camera or joint orders, or physical hardware. The packaged .pt file requires trusted PyTorch deserialization and the matching StarVLA code.

License status

This target repository did not previously publish a Model Card or a separate LICENSE file. The checkpoint's weight license therefore needs maintainer confirmation; the Qwen3-VL base-model terms and applicable dataset terms still apply.