ReNFT: Repairing Mode Collapse in Reward Post-Training via Internal Probability-Mass Recalibration

Reward post-training of diffusion generators inevitably concentrates probability mass on a few reward-favored modes, a mode collapse that erases within-prompt diversity. Existing methods for mitigating collapse rely on external signals or interfaces, augmenting the reward with perceptual objectives,