Mean-Flow based One-Step Vision-Language-Action

• URL: http://arxiv.org/abs/2603.01469v1
• Date: Mon, 02 Mar 2026 05:30:30 GMT
• Title: Mean-Flow based One-Step Vision-Language-Action
• Authors: Yang Chen, Xiaoguang Ma, Bin Zhao,
• Abstract summary: FlowMatching-based Vision-Language-Action (VLA) frameworks have demonstrated remarkable advantages in generating high-frequency action chunks.<n>They are constrained by prolonged generation latency, which stems from inherent iterative sampling requirements and architectural limitations.<n>We propose a Mean-Flow based One-Step VLA approach, which resolves the noise-induced issues in the action generation process.
• Score: 15.497933767026568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in FlowMatching-based Vision-Language-Action (VLA) frameworks have demonstrated remarkable advantages in generating high-frequency action chunks, particularly for highly dexterous robotic manipulation tasks. Despite these notable achievements, their practical applications are constrained by prolonged generation latency, which stems from inherent iterative sampling requirements and architectural limitations. To address this critical bottleneck, we propose a Mean-Flow based One-Step VLA approach. Specifically, we resolve the noise-induced issues in the action generation process, thereby eliminating the consistency constraints inherent to conventional Flow-Matching methods. This significantly enhances generation efficiency and enables one-step action generation. Real-world robotic experiments show that the generation speed of the proposed Mean-Flow based One-Step VLA is 8.7 times and 83.9 times faster than that of SmolVLA and Diffusion Policy, respectively. These results elucidate its great potential as a high-efficiency backbone for VLA-based robotic manipulation.

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