Long-Tailed Learning Strategies Matter in Machine Unlearning

Real-world datasets often exhibit long-tailed distributions, where head groups contain many samples and tail groups contain few. Although long-tailed learning strategies are widely used, how they affect machine unlearning remains unclear. In this paper, we study how such strategies influence unlearning difficulty. We find that strategies improving tail performance often make unlearning harder, producing larger deviations from retraining. Moreover, the same degree of tail exposure does not guarantee similar forgetting behavior. Beyond these findings, we observe domain-specific characteristics in vision and language across CIFAR-10, CIFAR-100, Tiny-ImageNet, and long-tailed variants of TOFU and ELUDe, using resampling, augmentation, and loss reweighting under class-wise and random-sample forgetting. Our findings suggest that long-tailed learning strategies should be carefully considered when comparing unlearning methods.