Distribution-wise Rewards Mitigate Mode Collapse in Visual Generation

A new framework optimizes visual generative models using distribution-wise rewards rather than sample-wise rewards. This approach prevents reward hacking and mode collapse by accounting for the entire data distribution during fine-tuning.