ReFPO: フローマッチングポリシー勾配のためのリフロー正則化
ReFPO: Reflow Regularization for Flow Matching Policy Gradients
フローマッチングポリシー勾配法に明示的なリフロー正則化を追加したオンライン強化学習手法を提案し、生成ポリシーの学習安定性と性能を向上させた。
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著者: Ge Wang, Yibo Peng, Fan Feng, Shenhao Yan, Chengsi Yao, Jiahao Yang, Honghao Cai, Yiming Zhao, Xi Li, Jinke Ren, Shuguang Cui, Yatong Han, Zhen Li
分類: cs.RO
原文アブストラクト
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.
関連論文
- 事前分布マッピング共進化による拡散ポリシーの学習強化学習/生成ポリシー
