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