Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness

Abstract:Machine unlearning aims to make a model forget specific data, yet unlearned LLMs often fail to stay unlearned: brief fine-tuning can revive removed knowledge. Existing robustness predictors rely on global weight-space displacement, but distance alone can be misleading when random or destructive updates collapse performance. We argue that relearning robustness depends on update structure: robust unlearning should affect forget-critical weights while sparing retain-critical ones. We introduce the Forget-Retain Alignment Gap (FRAG), a training-free predictor that scores an update's forget-retain alignment without running a relearning attack, and separates selective from dense updates more reliably than global distance. Building on the forget-critical, retain-sparing principle, Forget-Retain Pruning (FRP) improves relearning robustness. Our results suggest that weight selectivity better explains robustness than distance alone. Code is available at this https URL.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.25429 [cs.AI]

(or arXiv:2608.25429v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.25429 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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