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FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence

Authors: Li, Yinhao; Mao, Weixin; Lan, Zihan; Rong, Jikun; Zhu, Minzhao; Mao, Yiming; Shen, Bowen; +1 Authors

FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence

Abstract

Overview FluxVLA v0.1.2 expands FluxVLA with end-to-end SARM support, new model families (XVLA and Qwen3-VL), simulation and data-collection tooling (FluxBiSim and FluxDAgger), and broader hardware compatibility including Blackwell GPUs. Highlights Added end-to-end SARM support, including training, manual / VLM-based subtask annotation, and progress inference on LeRobot v2.1 / v3.x datasets, with a dedicated annotation toolkit and RABC weighting utilities. Added the XVLA model with a Florence2 backbone and flow-matching head. Added Qwen3-VL backbone support (including the Qwen3VL 0.6B + GR00T configuration). Added FluxBiSim training and inference support, plus documentation for the FluxBiSim simulation benchmark and the FluxDAgger dual-arm DAgger pipeline. Added Blackwell GPU (RTX 5090) compatibility support. Added PI and DreamZero configs, and tuned SmolVLA LIBERO finetune hyperparameters. Fixed a redundant image resize in the LIBERO eval pipeline and avoided duplicate training progress logs. Upgraded to transformers==5.3.0 (existing v0.1.0 environments can upgrade in place without recreating the conda env). Documentation This release adds documentation for SARM workflows (docs/sarm.md and tools/sarm_annotate/README.md), refreshes the README with a Performance benchmark table and updated Latest News, adds a Contributing guide, and documents the transformers upgrade path for existing installations.

If you use FluxVLA in your research, please cite it as below.

Keywords

Vision-Language-Action Models (VLA), World-Action-Model (WAM), Robotics, Embodied AI

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average