Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models

Jul 17, 2026

A new preprint demonstrates that generating chains of thought conditioned on the correct answer can significantly degrade the quality of reasoning data used for distilling large language models (LLMs). The study finds that this approach can reduce verifiable-reasoning accuracy by up to 27 points on the hardest problems, and that standard correctness filtering fails to detect the degradation. The authors recommend generating answer-blind chains of thought to avoid introducing hidden flaws into reasoning data.

Why it matters: This result challenges a common practice in LLM reasoning distillation and highlights the risk of introducing subtle but significant flaws when conditioning on answers.

Full story at: arXiv Computation and Language ↗