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arXiv Computation and Language By Xue Zhang, Songming Zhang, Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu

Meet Students Where They Are: Student-Prefix Teacher Adaptation for On-Policy Distillation

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The paper introduces Student‑Prefix Teacher Adaptation (SPTA), a method that trains a teacher model to better handle student‑generated prefixes before performing on‑policy distillation (OPD). By collecting prefixes of random lengths and using reinforcement learning with verifiable rewards (RLVR), the teacher learns to recover from student‑induced states rather than simply imitate the student’s reasoning. Experiments on mathematical reasoning benchmarks show that SPTA improves student performance compared to standard OPD and OPD with task‑level RLVR‑adapted teachers.

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