AdvDMD: Adversarial Reward Meets DMD For High-Quality Few-Step Generation

arXiv:2604.28126v1 Announce Type: cross Abstract: Diffusion models offer superior generation quality at the expense of extensive sampling steps. Distillation methods, with Distribution Matching Distillation (DMD) as a popular example, can mitigate this issue, but performance degradation remains pronounced when sampling steps are limited. Reinforcement learning (RL) has been leveraged to improve the few-step generation quality during distillation, with the potential to even surpass the performanc