From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space

Group Relative Policy Optimization (GRPO) has emergedas a powerful framework for preference alignment in text-to-image (T2I)flow models. However, we have observed that the standard paradigmthat evaluates a group of generated samples against a single conditionsuffers from insufficient exploration of inter-sample relationships, con-straining both alignment efficacy and performance ceilings. To addressthis sparse single-view evaluation scheme, we propose Multi-View GRPO(MV-GRPO), a novel algorithm that enhances relationship explorationby augmenting the condition space to create a dense multi-view rewardmapping. Specifically, for a group of samples generated from one prompt,MV-GRPO leverages a flexible Condition Enhancer to generate seman-tically adjacent yet diverse captions. These captions enable multi-viewadvantage re-estimation, capturing diverse semantic attributes and pro-viding richer optimization signals. By deriving the probability distribu-tion of the original samples conditioned on these new captions, they canbe incorporated into the training process without costly sample regen-eration. Extensive experiments demonstrate that MV-GRPO achievessuperior alignment performance over state-of-the-art methods.