Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies

2026-09-17

AI Summary

研究提出Coda方法，通过减少步骤并引入端点校正提高VLA策略的性能和效率，解决了增加集成步骤不必然提升闭环成功的问题。

Abstract

Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the number of integration steps raises inference cost, but does not necessarily improve closed-loop success. We propose Coda, which reallocates part of this integration budget to a single learned endpoint correction. A frozen policy first completes a few-step noise-to-action trajectory; a lightweight Transformer then predicts a demonstration-supervised residual using the candidate action, source noise, and shared observation-prefix cache. Only the corrector is trained. On 50 RoboTwin Easy tasks, five-step Coda improves success from 71.64% to 74.68% over the matched five-step baseline, while reducing forward latency by 30.2% relative to the default ten-step policy. A two-step configuration achieves 71.88% success with a 2.12$\times$ speedup. An independent 13-task control shows a 5.69-percentage-point gain at nearly equal latency, supporting correction as an effective alternative to additional integration. The same design also improves frozen official SmolVLA, raising two-step success from 60.8% to 69.4%. These results show that endpoint correction improves the quality-latency trade-off of frozen flow-matching policies.

Problem

• Vision-language-action
• Flow matching
• Inference cost
• Closed-loop success

Innovation

• Coda
• Endpoint Correction
• Flow Matching
• Transformer
• Latency Reduction