AdvantageFlow proposes an advantage-weighted forward-process least-squares loss for RL in rectified flow models, stabilized by rollout policy regularization, and reports better image generation performance than Flow-GRPO on Stable Diffusion 3.5.
Stepwise credit assignment for grpo on flow- matching models
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Super-Linear Advantage Shaping (SLAS) introduces a non-linear geometric policy update for RL post-training of text-to-image models that reshapes the local policy space via advantage-dependent Fisher-Rao weighting to reduce reward hacking and improve performance over GRPO baselines.
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AdvantageFlow: Advantage-Weighted Least Squares for RL in Flow Models
AdvantageFlow proposes an advantage-weighted forward-process least-squares loss for RL in rectified flow models, stabilized by rollout policy regularization, and reports better image generation performance than Flow-GRPO on Stable Diffusion 3.5.