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A multi-family task-description critic improves validation success from 46.0% to 56.0%, while the locked held-out test gain is positive but modest, from 65.0% to 67.5%","title":"Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies","url":"https://arxiv.org/abs/2607.02092","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.02092v2 Announce Type: replace-cross \nAbstract: Flow-matching vision-language-action policies generate robot action chunks through an iterative transport process, creating an opportunity for test-time guidance without retraining the base policy. We study this opportunity in Guided Action Flow, an inference-time framework that keeps a pretrained SmolVLA policy frozen and uses a learned action-chunk critic to guide its reverse-time flow sampler. 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