Meta-Rewarding Language Models: Self-Improving Alignment with
  LLM-as-a-Meta-Judge
Authors: name: Tianhao Wu, name: Weizhe Yuan, name: Olga Golovneva, name: Jing Xu, name: Yuandong Tian, name: Jiantao Jiao, name: Jason Weston, name: Sainbayar Sukhbaatar

Large Language Models (LLMs) are rapidly surpassing human knowledge in many
domains. While improving these models traditionally relies on costly human
data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs
can improve by judging their own responses instead of relying on human
labelers. However, existing methods have primarily focused on improving model
responses rather than judgment capabilities, resulting in rapid saturation
during iterative training. To address this issue, we introduce a novel
Meta-Rewarding step to the self-improvement process, where the model judges its
own judgements and uses that feedback to refine its judgment skills.
Surprisingly, this unsupervised approach improves the model's ability to judge
{\em and} follow instructions, as demonstrated by a win rate improvement of
Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2, and 20.6% to 29.1% on
Arena-Hard. These results strongly suggest the potential for self-improving
models without human supervision.