Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
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This February 13, 2026 Tencent research introduces Generalized On-Policy Distillation (G-OPD), a framework that refines how smaller AI models learn from larger or specialized teachers. By establishing a mathematical link between distillation and **reinforcement learning**, the authors demonstrate that traditional methods are limited by a rigid weighting of rewards. They propose **ExOPD**, a technique using **reward extrapolation** to push student models beyond the performance boundaries of their teachers in mathematical and coding tasks. The study further identifies **reward correction** as a vital tool for improving accuracy when distilling knowledge from massive models into compact ones. Ultimately, this framework enables a single student model to effectively **merge expertise** from multiple domain-specific teachers.
Source:
February 13, 2026
Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
Gaoling School of Artificial Intelligence, Renmin University of China; LLM Department, Tencent
Wenkai Yang, Weijie Liu, Ruobing Xie, Kai Yang, Saiyong Yang, Yankai Lin
https://arxiv.org/pdf/2602.12125
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