Reinforcement Learning for Flow-Matching Policies with Density Transport
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Reinforcement Learning for Flow-Matching Policies with Density Transport
Announce Type: new Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems. Our key insight is to view RL-based policy improvement as a transport of action densities towards regions of high reward, which naturally aligns with the transport formulation of flow matching models. Prior methods either approximate the current or optimal policy distribution or resort to distillation, which introduces biased gradients...