让AI忘掉学过的数据，顺便提速
现在的AI生成模型，训练时见过什么，生成时就会带出什么——包括不该有的隐私数据。这篇论文给了一个新办法：在把笨重的多步生成模型压缩成一步快模型的同时，顺手把指定类别的数据从记忆里抹掉。它不需要重新训练，也不需要保留数据，只要拿一个已经训练好的老师和一份要遗忘的样本，就能让模型在生成时几乎不再出现那些类别，同时保留其他类别的质量。这不是你明天能用的工具，但它指向一个关键趋势：AI的遗忘能力正在成为和生成能力一样重要的指标。
📄 原文摘要(英文)
Multi-step matching models, including flow and diffusion models, produce high-quality outputs but incur substantial inference costs and may reproduce unwanted components of their training datasets. We introduce Inverse Distillation Unlearning (IDU), a unified framework that simultaneously distills a teacher multi-step matching model into an efficient one-step student generator and suppresses outputs corresponding to a designated training subset. We first formulate distillation as a min-max objective over a data distribution and then represent this distribution as a mixture of the forget-set and the generated distributions. This allows us to compare this mixture with the teacher's training distribution and recover only the retained data at the optimum. Our method requires only a pretrained full-data teacher and data from the forget set, without access to retained training examples, extra feature extractors or classifiers. Extensive experiments on MNIST and CIFAR-10 datasets under flow-matching and score-based diffusion settings demonstrate that IDU substantially reduces the generation frequency of forgotten classes while preserving generation quality on the retained classes. To the best of our knowledge, IDU is the first unified framework for simultaneous unlearning and distillation in unconditional flow-matching and score-based models.