arXiv AI By Zhengming Yu, Junkun Yuan, Haotian Yang, Gordon Guocheng Qian, Yizhi Wang, Angtian Wang, Yiding Yang, Bo Liu, Xin Li, Wenping Wang, Chongyang Ma

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

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arXiv AI
5d ago

Persistent Negatives for Adversarial Black-Box On-Policy Distillation

The paper introduces Persistent Negative Adversarial Distillation, a method that improves black-box on-policy distillation by maintaining a live pool of historical teacher–student comparisons to stabilize the discriminator’s negative distribution. By anchoring the discriminator with these persistent negatives, the approach reduces reward-estimation error and yields smoother, higher-performing student policies across multiple benchmarks. The study demonstrates that the choice of negative samples is a critical design factor in effective black-box distillation.

By Haixu Ma, Saad Lahrichi, Weiwei Li, Kevin Han, Weiqiang Wu, Peggy Yang, Dongzhuo Li, Ruiyi Li, Serena Li, Gedi Zhou, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Lizhu Zhang