Understanding Machine Unlearning Through the Lens of Mode Connectivity
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 ...
| Authors | Jiali Cheng, Hadi Amiri |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 3 August 2026 |
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