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 first generates actions from Gaussian noise using the current flow policy and then uses a finite-...
| Authors | Bumgeun Park, Hyukjun Yang, Donghwan Lee |
|---|---|
| Primary category | cs.LG |
| Published | 14 September 2026 |
| Last updated at source | 27 September 2026 |
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