Qwen-FAST-Bridge-RT-1

StarVLA QwenFAST for Bridge + RT-1 (10K)

This repository contains the uploaded 10,000-step checkpoint from the 1003_qwenfast run. It is configured as a StarVLA QwenFast policy trained on the bridge_rt_1 mixture and evaluated on the four SimplerEnv WidowX tasks whose logs are published beside the checkpoint. QwenFast encodes continuous action chunks with the FAST tokenizer and trains the VLM by autoregressive next-token prediction; it is not a DiT or diffusion policy.

Model details

The YAML retains legacy dino, layer_qformer, and action_model_type: DiT-B fields, but the selected QwenFast implementation does not construct those components: it uses the FAST tokenizer and the VLM language-model loss. The action-model section does not declare state_dim, while the packaged raw statistics contain an 8D state for both datasets. Those statistics are a dataset artifact, not evidence that a numeric state vector enters this policy.

Training details

The YAML records the run target, not the released training extent. No weight later than 10K is present in this repository.

SimplerEnv WidowX evaluation

The repository includes four run logs for each of four WidowX tasks. Each completed log reports the standard 24-episode SimplerEnv success average. The table below is the unweighted mean of the four published run-level Average success values for this exact 10K checkpoint.

The resulting 58.6% rounded value matches the entry for this repository in the StarVLA Model Zoo. These are simulation results; they do not establish real-robot performance.

Files

config.yaml
dataset_statistics.json
checkpoints/
├── steps_10000_pytorch_model.pt
└── steps_10000_pytorch_model_infer_*.log.run{1,2,3,4}

Loading and evaluation

huggingface-cli download StarVLA/Qwen-FAST-Bridge-RT-1 \
  --local-dir Qwen-FAST-Bridge-RT-1

export CKPT=$PWD/Qwen-FAST-Bridge-RT-1/checkpoints/steps_10000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --port 10093 \
  --use_bf16

Use the maintained SimplerEnv integration for the client. Select bridge_dataset for WidowX evaluation; the second statistics key belongs to the RT-1/Fractal component of the training mixture.

Intended use and limitations

This checkpoint is intended for manipulation-policy research and SimplerEnv WidowX evaluation with a single RGB view and 7D delta-end-effector actions. The exact upstream VLM revision is absent from the Hub config. It is not a Transformers model directory, has not been shown safe on physical hardware, and is not safety-tuned. Loading requires the matching action-token vocabulary and FAST tokenizer; treating the stale DiT-B YAML label as the runtime decoder would reconstruct the wrong model.