Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization.

arXiv:2604.24952v3 Announce Type: replace-cross Abstract: Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulti