StarVLA/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

Item	Published configuration
Framework	StarVLA QwenFast
VLM	Local snapshot named Qwen2.5-VL-3B-Action; revision not recorded
Action encoding	FAST tokenizer over continuous action chunks
Training objective	Autoregressive cross-entropy over mapped action tokens
Inference	VLM token generation followed by FAST-token decoding
Action dimension / representation	7 / delta_ee
Numeric policy-state input	None in the checked QwenFast forward and inference paths
Action chunk	16 steps
Camera input	image_0, 224 x 224
Dataset mixture	bridge_rt_1 (Bridge + RT-1/Fractal)
Normalization keys	bridge_dataset, fractal20220817_data
Uploaded checkpoint	checkpoints/steps_10000_pytorch_model.pt

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

Setting	Value in config.yaml
Intended maximum steps	100,000
Released and evaluated step	10,000
Per-device VLA batch size	16
Gradient accumulation	1
Warm-up steps	10,000
Base / interface / action LR	3e-5 / 1e-5 / 1e-4
Optimizer	AdamW, betas (0.9, 0.95), epsilon 1e-8
Scheduler	Cosine with minimum LR 5e-7
VLA / VLM loss scale	1.0 / 0.1
Packaged freeze_modules	Boolean true; the public trainer only accepts a comma-separated string, so this does not select modules to freeze
Seed	42

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.

Task	Success rate
PutCarrotOnPlateInScene-v0	0.4167
PutEggplantInBasketScene-v0	0.8438
PutSpoonOnTableClothInScene-v0	0.7188
StackGreenCubeOnYellowCubeBakedTexInScene-v0	0.3646
Mean over all 16 run logs	0.5859

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.