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GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
Recent advances in large reasoning models (LRMs) have made machine unlearning more challenging, as protected facts or unsafe rationales may surface in intermediate chain-of-thought (CoT) traces before the final answer is produced. Existing unlearning objectives typically suppress the target content
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