arXiv — cs.AI preprintsInternational12 October 2026
Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
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arXiv:2605.21282v3 Announce Type: replace-cross Abstract: Combining entropy regularisation with mirror descent in online off-policy reinforcement learning requires expressive policies with efficient sampling and explicit entropy control. Gaussian policies struggle to represent multimodal targets, whereas generative policies often require iterative sampling or lack tractable entropy estimates. We introduce Stochastic MeanFlow Policies (SMFP), a one-step generative policy class that combines MeanFlow-based noise-to-action mappings with Gaussian reparameterisation. The latent mapping captures div
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