ByteDance and Texas A&M University · Few-Step Distillation

DMAD

Distribution Matching as Adversarial Distillation, from Texas A&M and ByteDance, posted 1 October (arXiv 2610.02188). It turns distribution matching into a classification problem: two discriminator heads on one shared backbone learn density ratios directly, so no extra auxiliary models are needed. The authors report FID 1.04 for one-step ImageNet-64 generation, 84.6% human preference over rCM on Wan2.1-T2V-14B and 79.1% preference over DMD2 on MiniMax-H3 (covered here in week 32), all self-reported. Code is on GitHub under Apache-2.0 and weights are on Hugging Face.