Understanding On-Policy Distillation: A Mechanistic Interpretability Perspective via Sparse Crosscoders
On-policy distillation (OPD) is a widely adopted post-training technique for LLM reasoning. It is commonly believed to transfer knowledge from a stronger teacher, yet what OPD actually distills into the student's internal representations remains unclear. We study this question with sparse crosscoders, which learn one feature dictionary shared by the student before and after OPD and the teacher. Standard crosscoder analyses, however, identify model-specific features but cannot tell how a model's use of its features changes, since all models are encoded into one set of feature activations. We therefore propose the swap readout, which reads each student checkpoint's feature activations on it...
| Authors | Zichao Yu, Qianshuo Ye, Xu Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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