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Negative Self-Distillation: Learning to Reason by Avoiding Flaws

Research LLMs & Foundation Models

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TL;DR - Negative Self-Distillation (NSD) improves LLM reasoning by teaching models to diverge from self-generated flawed reasoning rather than imitate privileged, artificially confident solutions. This preserves exploratory and self-corrective behavior that conventional on-policy self-distillation may suppress.

  • NSD requires neither ground-truth answers nor external supervision; the model generates a question-specific negative persona, such as a “careless reasoner.”
  • A dynamic gating mechanism isolates reasoning-critical tokens so training targets behavioral flaws without degrading foundational language abilities.
  • The approach addresses the confounding of flawed reasoning with ordinary linguistic tokens that makes naive unlearning objectives risky.
  • NSD reportedly outperforms on-policy self-distillation and other label-free, self-bootstrapping reinforcement-learning baselines.

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Negative Self-Distillation: Learning to Reason by Avoiding Flaws

arXiv cs.CL Rongcan Pei, Zhepei Wei, Shuyao Xu, Xinyu Zhu, Wei-Lin Chen, Yu Meng 2026-09-10 arXiv:2609.11699
Public signals Hugging Face upvotes 35
Providers: Hugging Face · Upvotes 35 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:21:04.106179 UTC

TL;DR - Negative Self-Distillation (NSD) improves LLM reasoning by teaching models to diverge from self-generated flawed reasoning rather than imitate privileged, artificially confident solutions. This preserves exploratory and self-corrective behavior that conventional on-policy self-distillation may suppress.

  • NSD requires neither ground-truth answers nor external supervision; the model generates a question-specific negative persona, such as a “careless reasoner.”
  • A dynamic gating mechanism isolates reasoning-critical tokens so training targets behavioral flaws without degrading foundational language abilities.
  • The approach addresses the confounding of flawed reasoning with ordinary linguistic tokens that makes naive unlearning objectives risky.
  • NSD reportedly outperforms on-policy self-distillation and other label-free, self-bootstrapping reinforcement-learning baselines.
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