arXiv AI October 12, 2026 By Zeyuan Wang, Da Li, Yulin Chen, Yuehu Gong, Yanming Guo, Liang Bai, Tianyuan Yu, Yanwei Fu

Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent

The paper introduces Stochastic MeanFlow Policies (SMFP), a one‑step generative policy class that merges MeanFlow noise‑to‑action mappings with Gaussian reparameterisation. SMFP captures diverse action modes while providing an analytic entropy lower bound through a learnable conditional Gaussian scale, enabling entropy‑regularised mirror‑descent learning. Experiments on seven MuJoCo benchmarks show SMFP outperforming Gaussian and generative baselines, and additional tests demonstrate effective multimodal action modelling and exploration in sparse‑reward navigation.

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