AdvantageFlow: Regularized Advantage-Weighted RL in Flow Models

Abstract:We present AdvantageFlow, a forward-process reinforcement learning (RL) algorithm for rectified flow models. The algorithm minimizes an advantage-weighted prediction loss, which maximizes reward, regularized by the rollout policy, which convexifies the objective and makes its optimization stable. Our objective can be viewed as fitting a local reward-improving target distribution. The rollout regularization arises as a variance reduction step. We evaluate AdvantageFlow empirically on text-to-image generation with Stable Diffusion 3.5 Medium and FLUX.1, and compare it to both forward- and reverse-process RL algorithms.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2605.26013 [cs.LG]

(or arXiv:2605.26013v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.26013 Focus to learn more arXiv-issued DOI via DataCite

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