ReFPO: Reflow Regularization for Flow Matching Policy Gradients
For researchers in reinforcement learning and generative modeling, ReFPO provides a scalable and stable approach to flow-based policies in complex physical simulations.
ReFPO introduces a simple online RL method that adds explicit Reflow regularization to Flow Matching Policy Gradients, stabilizing training and enabling high-fidelity one-step inference that matches or exceeds multi-step performance across GridWorld, MuJoCo Playground, and Humanoid Control tasks.
We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for efficient flow-based control. We uncover a key structural property: the gradient updates in Flow Matching Policy Gradients (FPO) can be interpreted as an implicit advantage-weighted Reflow process, providing a new geometric perspective on flow-based policy gradients. Building on this insight, ReFPO introduces an explicit geometric regularizer that can be implemented with a single line of code change without incurring additional computational overhead or auxiliary distillation stages. By synergizing advantage-guided updates with path rectification, our method reduces CFM proxy-ratio spikes, stabilizes PPO-style training, and enables high-fidelity one-step inference that often matches or exceeds multi-step performance. We experimentally demonstrate that ReFPO improves average performance and discretization robustness across GridWorld, MuJoCo Playground, and high-dimensional Humanoid Control tasks, providing a scalable and stable approach for generative policies in complex physical simulations.