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Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
Preference optimization has emerged as an efficient alternative to online reinforcement learning from human feedback (RLHF) for aligning text-to-image diffusion models. However, existing methods largely reduce supervision to binary pairwise comparisons. This pairwise reduction is limiting when train
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