Self-Rewarding Language Models
Weizhe Yuan1,2
Richard Yuanzhe Pang1,2
Kyunghyun Cho2
Sainbayar Sukhbaatar1
Jing Xu1
Jason Weston1,2
1 Meta
2 NYU
Abstract
We posit that to achieve superhuman agents, future models require super-
human feedback in order to provide an adequate training signal. Current
approaches commonly train reward models from human preferences, which
may then be bottlenecked by human performance level, and secondly these
separate frozen reward models cannot then learn to improve during LLM
t