Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies
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 74.6...
| Authors | Zhipeng Tang, Xinda Chen, Weining Rao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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