Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation

Di Wu; Rongtian Shen; Ping Liu; Yan Shen; Zhenhan Yin; Shun Zuo; Xuhua Chen; He Zheng; Lingfeng Zhang; Jianglin Zhang; Tao Zhang

title	Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation
authors	Di Wu, Rongtian Shen, Ping Liu, Yan Shen, Zhenhan Yin, Shun Zuo, Xuhua Chen, He Zheng, Lingfeng Zhang, Jianglin Zhang, Tao Zhang
date	2026-10-03
venue	arXiv (preprint, not peer reviewed)
id	arXiv:2609.39822
upvotes	2 (Hugging Face Daily Papers, 2026-11-01)
links	abs pdf hf code
primer	Vision-language-action models
abstract	Vision-language-action (VLA) models face a timing gap between low-rate inference and high-rate robot execution. We characterize this gap through end-to-end latency measurements of model inference and the robot execution chain. Repeated Flow Matching denoising contributes substantially to inference cost, while robot-side delays mainly arise from perception acquisition, communication scheduling, and physical response. Analysis of the velocity field shows relatively stable magnitude and direction in early integration, followed by stronger directional correction near the terminal steps. Based on this stage heterogeneity, we propose two-stage non-uniform denoising, reducing the number of steps from 10 to 2 and model-inference time from 61.557 ms to 21.956 ms. We also develop a distributed real-time VLA framework with independent inference, action-publication, and robot-control rates, modular observation acquisition, and action-provenance logging. Using π0.5 as the baseline, we evaluate six real-time execution methods on a long-horizon physical garment-folding task. Legato performs best overall among training-based methods, while Temporal Smoothing leads among training-free methods; both perform strongly in task success, completion time, action continuity, and acceleration smoothness. Combining two-step denoising with representative execution methods substantially reduces inference cost with a small reduction in task performance. These results motivate joint optimization of model-inference efficiency and robot-system timing.

cite

@misc{wu2026toward,
  title={Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation},
  author={Di Wu and Rongtian Shen and Ping Liu and Yan Shen and Zhenhan Yin and Shun Zuo and Xuhua Chen and He Zheng and Lingfeng Zhang and Jianglin Zhang and Tao Zhang},
  year={2026},
  eprint={2609.39822},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.39822}
}