SOURCE-LINKED INTELLIGENCE
Refinement-based Flow Policy Optimization
Flow-based policies offer an expressive representation for online reinforcement learning, but conventional flow matching requires samples drawn from the distribution to be modeled. This poses a challenge when the desired action distribution is defined only implicitly by a Q-function, since directly sampling actions from the resulting distribution is generally intractable. We propose Refinement-Based Flow Policy Optimization (RFPO), a novel framework for training a flow policy in online reinforcement learning by alternating between Q-guided sample refinement and self-target flow matching. RFPO
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T06:56:33.000Z
First collected: 2026-09-20T11:41:07.830Z. This is not the publication date.