Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors

arXiv:2608.16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data D_f removed from a machine learning model. This process is typically accomplished via the use of an unlearning function denoted as U. Existing methods focus on designing an intricate U to unlearn D_f subset D from a previous model A(D), so that the unlearned model performs as closely as possible to the retrained model A(D setminus D_f). However, these methods often suffer from high computational costs when dealing with massive training data, as the complex structures of U become a bottleneck even for models with fewer parameters. Inspired by Learning to Optimize, we introduce the first learning-based model-agnostic approach, Learning-to-UnLearn (L2UL). Our core insight is to shift from manually designing U to learning the unlearning behaviors from a distribution perspective, thereby acquiring a simple and efficient U via learning. Our experimental results demonstrate that the accuracy achieved by L2UL is comparable to that of retraining while exhibiting impressive efficiency, particularly in data-intensive scenarios. Furthermore, we validate the performance and scalability of our method on larger models ResNet.

Source: arXiv cs.AI | 2026-08-18