Negative Self-Distillation: Learning to Reason by Avoiding Flaws

• URL: http://arxiv.org/abs/2609.11699v1
• Date: Thu, 10 Sep 2026 15:24:17 GMT
• Title: Negative Self-Distillation: Learning to Reason by Avoiding Flaws
• Abstract summary: On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement.<n>OPSD forces the student to imitate an artificially confident reasoning trace conditioned on privileged information.<n>We introduce Negative Self-Distillation (NSD) to optimize LLMs by diverging from flawed reasoning rather than imitating privileged solutions.
• Score: 17.066626113937463
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their own teachers by leveraging privileged information such as ground-truth solutions. However, recent findings indicate that OPSD can severely degrade the performance of LLMs on complex reasoning tasks: By forcing the student to imitate an artificially confident reasoning trace conditioned on privileged information, OPSD inadvertently suppresses expressions of uncertainty and penalizes the exploratory, self-corrective behaviors required to solve challenging problems. To address this, we introduce Negative Self-Distillation (NSD), a new framework that optimizes LLMs by diverging from flawed reasoning rather than imitating privileged solutions. Instead of relying on ground-truth answers or external supervision, NSD uses the model itself to generate a question-specific negative condition (eg, acting as a ``careless reasoner'') and pushes the student's distribution away from this self-generated negative teacher. Naively applying unlearning objectives to achieve this divergence is problematic, as flawed reasoning tokens are confounded with basic linguistic tokens; indiscriminately penalizing both risks catastrophically degrading the model's foundational language capabilities. We resolve this by designing a dynamic gating mechanism that automatically identifies and isolates reasoning-critical tokens, ensuring gradient updates target only behavioral flaws while preserving the model's linguistic priors. Empirically, NSD consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning (RL) baselines.

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