Inverse Distillation Unlearning Enables One-Step Model Distillation and Data Removal

Inverse Distillation Unlearning (IDU) provides a unified framework to distill multi-step matching models into efficient one-step generators while simultaneously suppressing specific training subsets. The method formulates distillation as a min-max objective over a mixture of forget-set and generated distributions.