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News/From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning
arxiv
PublishedOctober 15, 2026 at 4:00 AM

From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning

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arXiv:2610.16984v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as diffusion and flow matching enhance policy expressiveness, they typically start from uninformative

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