#flow-models

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2 curated events

papersTODAY 04:00 UTC

Paper Proposes One-Step Flow Policy for Offline Reinforcement Learning

A new arXiv paper introduces a method for learning multimodal one-step flow policies from fixed offline datasets using value-weighted optimal transport. The approach targets offline reinforcement learning, where action distributions are often multimodal and existing flow policies are slow to sample. It appears in both cs.AI and cs.LG cross-listings.

reinforcement-learning/flow-models/optimal-transport/research

papersSEP 10 04:00 UTC

FlowCPO paper unifies online RL and offline preference alignment for flow and diffusion models

A new arXiv preprint presents FlowCPO, a framework that interprets preference alignment for flow and diffusion models through a single divergence-based lens. The work connects online reinforcement learning techniques with offline preference optimization, approaches that had previously been studied in isolation. The authors also examine limitations of existing forward-process alignment methods within this unified view.

flow-models/diffusion/preference-alignment/reinforcement-learning