aiqbench analysis
This research investigates machine unlearning by analyzing the optimization geometry of loss landscapes, specifically introducing "mode connectivity in unlearning" to understand how models transition between retaining knowledge and forgetting specific data. The study reveals that approximate unlearning methods operate through distinct mechanisms compared to full retraining and highlights nonlinear progressions where privacy metrics can vary significantly even within connected parameter basins. These findings provide critical geometric insights for improving model robustness against relearning attacks without necessitating complete dataset regeneration.
Understanding Machine Unlearning Through the Lens of Mode Connectivity
arXiv:2504.06407v2 Announce Type: replace-cross Abstract: Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss landscape and optimization geometry of unlearning are poorly understood. In this paper, we study machine unlearning through the lens of mode connectivity--the phenomenon that independently trained models can often be connected by smooth low-loss paths in parameter space. We introduce {\em mode connectivity in unlearning} (MCU) and evaluate it across a range of settings, including curriculum learning, second-order optimization, and connectivity across different unlearning methods. We find that many unlearned models lie in connected basins with smooth retain/forget behavior, while changes in training dynamics can move solutions into different basins. MCU also reveals that models within the same basin can differ substantially on privacy metrics, and that unlearning progresses nonlinearly from the original model to the unlearned model. In addition, linear connectivity suggests that most approximate unlearning methods are mechanistically distinct from retraining. Finally, MCU-based ensembling can improve generalization and robustness to relearning attacks, and MCU smoothness correlates with unlearning difficulty. To our knowledge, this is the first study of machine unlearning through the lens of mode connectivity.
Abstract:Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss landscape and optimization geometry of unlearning are poorly understood. In this paper, we study machine unlearning through the lens of mode connectivity--the phenomenon that independently trained models can often be connected by smooth low-loss paths in parameter space. We introduce {\em mode connectivity in unlearning} (MCU) and evaluate it across a range of settings, including curriculum learning, second-order optimization, and connectivity across different unlearning methods. We find that many unlearned models lie in connected basins with smooth retain/forget behavior, while changes in training dynamics can move solutions into different basins. MCU also reveals that models within the same basin can differ substantially on privacy metrics, and that unlearning progresses nonlinearly from the original model to the unlearned model. In addition, linear connectivity suggests that most approximate unlearning methods are mechanistically distinct from retraining. Finally, MCU-based ensembling can improve generalization and robustness to relearning attacks, and MCU smoothness correlates with unlearning difficulty. To our knowledge, this is the first study of machine unlearning through the lens of mode connectivity.
COLM 2026; Previously this version appeared as arXiv:2607.23970 which was submitted as a new work by accident
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2504.06407 [cs.LG]
(or arXiv:2504.06407v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2504.06407 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 310 words
