Mean Flow Policy Optimization
Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative processes introduce substantial training and inference overhead. To overcome this limitation, we propose to represent policies using MeanFlow models, a class of few-step flow-based generative models, to improve training and inference efficiency over diffusion-based RL approaches. To promote exploration, we optimize MeanFlow policies under the maximum entropy RL framework via soft policy iteration, and address two key challenges specific to MeanFlow policies: action likelihood evaluation and soft policy improvement. Experiments on MuJoCo, D...
| Authors | Xiaoyi Dong, Xi Sheryl Zhang, Jian Cheng |
|---|---|
| Primary category | cs.LG |
| Published | 16 April 2026 |
| Last updated at source | 30 May 2026 |
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