Negative Self-Distillation: Learning to Reason by Avoiding Flaws
Negative Self-Distillation (NSD) is introduced, a new framework that optimizes LLMs by diverging from flawed reasoning rather than imitating privileged solutions, and consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning (RL) baselines.
Rong-Can Pei, Zhepei Wei, Shu-Yao Xu et al.
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