[R] Self-Rewarding Language Models - Meta 2024

Paper: [https://arxiv.org/abs/2401.10020](https://arxiv.org/abs/2401.10020)

Github: [https://github.com/lucidrains/self-rewarding-lm-pytorch](https://github.com/lucidrains/self-rewarding-lm-pytorch)

Abstract:

>We posit that to achieve superhuman agents, future models require superhuman 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 training. In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own …

abstract agents current feedback future human human performance learn llm machinelearning performance posit signal study superhuman train training work