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arXiv · 2605.23365

Score-Based One-step MeanFlow Policy Optimization

Kyungyoon Kim·Donghyeon Ki·Hee-Jun Ahn·Byung-Jun Lee
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Abstract

Diffusion and flow matching have emerged as expressive policy classes in reinforcement learning, but their reliance on multi-step denoising imposes substantial computational overhead at inference time, which is particularly problematic in online RL. MeanFlow offers a promising alternative by learning an average velocity field that maps noise to data in a single network evaluation. However, MeanFlow typically requires samples from the target distribution to construct its target velocity field, which are unavailable in online RL. We propose Score-Based One-step MeanFlow Policy Optimization (SOM), an actor-critic algorithm that resolves this by constructing the target velocity field directly from the Q-function via score estimation and a probability flow ODE, thereby concentrating probability mass on high-value modes. In the fully online RL setting, SOM achieves state-of-the-art performance on locomotion tasks with a single generation step, while substantially reducing both training and inference time compared to prior diffusion- and flow-matching-based policies.

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Kyungyoon Kim, Donghyeon Ki, Hee-Jun Ahn, Byung-Jun Lee. 2026-05-22. Score-Based One-step MeanFlow Policy Optimization. https://arxiv.org/abs/2605.23365

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