Shuyang Gu

Optimizing Visual Generative Models via Distribution-wise Rewards

A novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world data distributions is presented, and a subset-replace strategy that efficiently provides reward signals by updating only a small subset of a generated reference set is introduced.