KARU：基于强化学习的 LLM 知识遗忘方法
What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations
unlearning 老问题的新解法：把要忘的知识定义成条件分布，再用强化学习精准抑制，比整句抹掉的方式误伤更小。
论文提出将知识关联（Knowledge Association, KA）作为 LLM 遗忘的基本目标，即查询上下文与目标知识之间的条件预测分布。基于 KA，作者提出 KARU 框架，用强化学习同时抑制需遗忘的知识关联并保留其余关联。方法包含 token 级奖励构造、分布级正则化和邻近感知的保留采样三项机制，以减少误伤其他能力。在多个 LLM 和代表性基准上的实验显示，KARU 的遗忘效果好于现有基线，保留性能与通用能力相当。
What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations
Large language models (LLMs) acquire extensive factual knowledge during pre-training, which may include sensitive, private, or copyrighted information, motivating the development of unlearning methods that selectively remove specific knowledge while preserving general capabilities. However, existing approaches typically optimize entire responses without explicitly identifying the knowledge to be forgotten, resulting in coarse and uncontrolled forgetting. To address this, we revisit the forgetting target of LLM unlearning and introduce Knowledge Association (KA), which characterizes the association between a query context and its target knowledge and is instantiated by the corresponding conditional predictive distribution, as the fundamental target of forgetting. Based on KA, we propose Knowledge Association Reinforcement Unlearning (KARU), a unified reinforcement learning framework that suppresses forgotten knowledge associations while preserving retained ones. Specifically, KARU derives complementary mechanisms from the KA-based formulation: token-level reward construction for finer-grained forgetting supervision, distribution-level regularization for precise knowledge suppression, and proximity-aware retain sampling to mitigate collateral degradation. Experiments across diverse LLMs on representative benchmarks demonstrate that KARU consistently improves forgetting effectiveness over existing baselines while maintaining comparable retention performance and general utility.