z-lab / flashvla-pi05-libero

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Introduction of flashvla-pi05-libero

Model Details of flashvla-pi05-libero

FlashVLA · π0.5 · LIBERO

Streaming Action Decoding for Fast and Asynchronous VLA Inference

Paper GitHub Blog Models

A π0.5 flow-matching vision-language-action policy finetuned on LIBERO and served with FlashVLA streaming action decoding for fast, asynchronous inference.

  • Base model: lerobot/pi05_base
  • Method: FlashVLA , streaming action decoding for flow-matching VLAs (async chunk-overlap execution)
  • Benchmark: LIBERO (Spatial / Object / Goal / Long)
Results

LIBERO success rate (%) and per-step latency. d is the async step delay: d=0 is synchronous, d>=1 overlaps the next chunk's inference with robot execution.

Model Spatial Object Goal Long Avg Time/Step (ms)
π0.5 (base) 98.8 98.2 98.0 92.4 96.9 53.8
+FlashVLA ( d=0 ) 98.6 99.0 97.8 96.2 97.9 —
+FlashVLA ( d=1 ) 96.0 99.6 96.4 96.0 97.0 29.4 (1.83× faster)
Usage

Install FlashVLA:

git clone https://github.com/z-lab/flashvla.git
cd flashvla
conda env create -f environment.yml
conda activate flashvla

LIBERO evaluation needs additional simulator-specific setup, described in sim_eval/libero/ . Once that is in place:

bash sim_eval/libero/eval.sh

Training configs for this checkpoint are in train/configs/pi05/libero/ .

License

These weights are finetuned from lerobot/pi05_base , which is released under the Gemma Terms of Use . Those terms govern model derivatives, so they apply to this checkpoint and to anything derived from it, including the Gemma Prohibited Use Policy . If you redistribute this checkpoint or a derivative of it, you must pass the same terms along.

The FlashVLA inference and training code is separately released under the Apache 2.0 License .

Citation
@inproceedings{li2026flashvla,
  title     = {{FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference}},
  author    = {Li, Zekai and Tang, Jiaming and Liu, Zhijian},
  booktitle = {Conference on Robot Learning (CoRL)},
  year      = {2026}
}

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