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October 9, 2025Open Access

One-Step Flow Policy Mirror Descent

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Flow Policy Mirror Descent enables efficient one-step inference in reinforcement learning, improving response times.
Algorithms show performance comparable to diffusion policy baselines, with significantly fewer function evaluations required.
Empirical evaluations on MuJoCo benchmarks support the effectiveness of the proposed approach in practice.
The method leverages a connection between distribution variance and sampling error, avoiding additional training complexities.

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Online RL algorithm demonstrates strong performance in sampling efficiency, highlighting advancements over diffusion policies.

Chen et al. (2025) studied this question.

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