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Dissecting Advantage-Guided Post-Training for Vision-Language-Action Policies

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However, its performance depends on several coupled choices, including how critic-derived advantages are constructed, calibrated, and used for policy training. Existing recipes often combine these choices into a single end-to-end procedure, making their individual effects difficult to identify. In this work, we dissect advantage-guided VLA post-training through a controlled empirical study that separates these design choices while accounting for their d

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