Score-Based One-step MeanFlow Policy Optimization
The paper introduces Score-Based One-step MeanFlow Policy Optimization (SOM), an innovative actor-critic algorithm designed for online reinforcement learning. SOM addresses the computational challenges of existing methods by constructing a target velocity field directly from the Q-function, allowing for efficient policy optimization. The results demonstrate that SOM achieves state-of-the-art performance on locomotion tasks while significantly reducing training and inference times.
- ▪SOM is an actor-critic algorithm that optimizes policy in online reinforcement learning.
- ▪It constructs the target velocity field from the Q-function using score estimation and a probability flow ODE.
- ▪SOM outperforms previous methods in locomotion tasks with a single generation step.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23365 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Machine Learning arXiv:2605.23365 (cs) [Submitted on 22 May 2026] Title:Score-Based One-step MeanFlow Policy Optimization Authors:Kyungyoon Kim, Donghyeon Ki, Hee-Jun Ahn, Byung-Jun Lee View a PDF of the paper titled Score-Based One-step MeanFlow Policy Optimization, by Kyungyoon Kim and 3 other authors View PDF HTML (experimental) 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.
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