Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why
Researchers have introduced a method called reward-gated on-policy distillation that improves how smaller AI models learn from more powerful teacher systems. By filtering the training data so that students only learn from trajectories that receive high rewards, the technique reduces the noise typical of standard imitation learning. This approach aims to make smaller, more efficient models better at complex reasoning tasks by ensuring they focus on high-quality outputs during the knowledge transfer process.
Covered by 2 sources · 6 articles
- AApple Machine Learning Blog↗1d ago
- AarXiv CS.AI↗Mohammad Sadegh Akhondzadeh, Vijay Lingam, Atula Tejaswi, Chanakya Ekbote, Sujay Sanghavi, Aleksandar Bojchevski3d ago
- AarXiv CS.AI↗Wenjin Hou, Shangpin Peng, Weinong Wang, Zheng Ruan, Yue Zhang, Zhenglin Zhou, Mingqi Gao, Yifei Chen, Kaiqi Wang, Hongming Yang, Chengquan Zhang, Zhuotao Tian, Han Hu, Yi Yang, Fei Wu, Hehe Fan3d ago
- AarXiv CS.AI↗Zhengpeng Xie, Li Lyna Zhang, Zeke Xie, Mao Yang3d ago
- AarXiv CS.AI↗Phuong Tuan Dat, Qi Li, Xinchao Wang3d ago
- AarXiv CS.AI↗Shiyuan Feng, Huan-ang Gao, Haohan Chi, Hanlin Wu, Zhilong Zhang, Zheng Jiang, Bingxiang He, Wei-Ying Ma, Ya-Qin Zhang, Hao Zhou3d ago