Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM unlearning as an asymmetric two-task problem: retention is the primary objective and forgetting is an auxiliary. From this perspective, we propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient extraction from conflict-aware combination. Instantiating the framework, we adapt established PCGrad to resolve gradient conflicts, and introduce SAGO, a novel retention-prioritized gradient synthesis method. Theoretically, both variants ensure non-negative cosine similarity with the retain gradient, while...
| Authors | Zeguan Xiao, Siqing Li, Yong Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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