Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

ICLR 2025 Conference Submission7921 Authors

26 Sept 2024 (modified: 02 Dec 2024)ICLR 2025 Conference SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: self-improving, self-rewarding, LLM, LLM-as-a-judge, instruction following, super alignment
TL;DR: The model judges its own responses to improve them, but now it judges its own judgements too to improve them as well
Abstract: 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 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.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7921
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