From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models. However, we observe that the standard paradigm where evaluating a group of generated samples against a single condition suffers from insufficient exploration of inter-sample relationships, constraining both alignment efficacy and performance ceilings. To address this sparse single-view evaluation scheme, we propose Multi-View GRPO (MV-GRPO), a novel approach that enhances relationship exploration by augmenting the condition space to create a dense multi-view reward mapping. Specifically, for a group of samples generated from one prompt, MV-GRPO leverages a flexible Condition Enhancer to generate semantically adjacent yet diverse captions. These captions enable multi-view advantage re-estimation, capturing diverse semantic attributes and providing richer optimization signals. By deriving the probability distribution of the original samples conditioned on these new captions, we can incorporate them into the training process without costly sample regeneration. Extensive experiments demonstrate that MV-GRPO achieves superior alignment performance over state-of-the-art methods.
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Jiazi Bu
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2026-03-23T11:03:34.193Z
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Azusa
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Pengyang Ling
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Yujie Zhou
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2026-03-23T10:59:10.456Z
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yujie
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Yibin Wang
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2026-03-16T10:40:14.158Z
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SII-Yibin Wang
CodeGoat24
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Yuhang Zang
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2026-03-16T10:40:17.636Z
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Yuhang Zang
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Tianyi Wei
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Xiaohang Zhan
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2026-03-16T11:07:36.620Z
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Xiaohang Zhan
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xhangzhan
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Jiaqi Wang
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Tong Wu
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Xingang Pan
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2026-03-16T11:07:49.439Z
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Xingang Pan
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Dahua Lin
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2026-03-16T11:07:55.742Z
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Dahua Lin
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From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models. However, we observe that the standard paradigm where evaluating a group of generated samples against a single condition suffers from insufficient exploration of inter-sample relationships, constraining both alignment efficacy and performance ceilings. To address this sparse single-view evaluation scheme, we propose Multi-View GRPO (MV-GRPO), a novel approach that enhances relationship exploration by augmenting the condition space to create a dense multi-view reward mapping. Specifically, for a group of samples generated from one prompt, MV-GRPO leverages a flexible Condition Enhancer to generate semantically adjacent yet diverse captions. These captions enable multi-view advantage re-estimation, capturing diverse semantic attributes and providing richer optimization signals. By deriving the probability distribution of the original samples conditioned on these new captions, we can incorporate them into the training process without costly sample regeneration. Extensive experiments demonstrate that MV-GRPO achieves superior alignment performance over state-of-the-art methods.
Multi-View GRPO enhances text-to-image flow model alignment by expanding condition space for richer reward mapping and improved sample relationship exploration.
Group Relative Policy Optimization text-to-image flow models preference alignment multi-view reward mapping condition space semantically adjacent captions advantage re-estimation probability distribution sample regeneration