arXiv AI
What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations
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Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
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Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models
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Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning
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