Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models
Reinforcement learning from human feedback (RLHF) effectively promotes preference alignment of text-to-image (T2I) diffusion models. To improve computational efficiency, direct preference optimization (DPO), which avoids explicit reward modeling, has been widely studied. However, its reliance on binary feedback limits it to coarse-grained modeling on chosen-rejected pairs, resulting in suboptimal optimization. In this paper, we propose ArenaPO, which leverages Arena scores as offline rewards to provide refined feedback, thus achieving efficient and fine-grained optimization without a reward model. This enables ArenaPO to benefit from both the rich rewards of traditional RLHF and the effic...
| Authors | Zhikai Li, Yue Zhao, Edward Zhongwei Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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