Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
A standard recipe for distilling the reasoning ability of large language models (LLMs) is to sample chains of thought from the model, keep those that reach the correct final answer, and fine-tune on the survivors. When sampling fails, a common fix shows the generator the gold answer and asks it to write a chain that reaches that answer. We show that this second step degrades the training data in a way that correctness filtering cannot catch. We run a controlled experiment that fixes the generator, the problem set, and the correctness filter, and varies only whether the chain is generated under answer-conditioning, the gold answer shown with a request to reach it. Training a strong instruc...
| Authors | Jungseob Lee, Seungyoon Lee, Suhyune Son et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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