title: Enabling Scalable Oversight via Self-Evolving Critic
summary: Despite their remarkable performance, the development of Large Language
  Models (LLMs) faces a critical challenge in scalable oversight: providing
  effective feedback for tasks where human evaluation is difficult or where LLMs
  outperform humans. While there is growing interest in using LLMs for critique,
  current approaches still rely on human annotations or more powerful models,
  leaving the issue of enhancing critique capabilities without external
  supervision unresolved. We introduce SCRIT (Self-evolving CRITic), a framework
  that enables genuine self-evolution of critique abilities. Technically, SCRIT
  self-improves by training on synthetic data, generated by a contrastive-based
  self-critic that uses reference solutions for step-by-step critique, and a
  self-validation mechanism that ensures critique quality through correction
  outcomes. Implemented with Qwen2.5-72B-Instruct, one of the most powerful LLMs,
  SCRIT achieves up to a 10.3\% improvement on critique-correction and error
  identification benchmarks. Our analysis reveals that SCRIT's performance scales
  positively with data and model size, outperforms alternative approaches, and
  benefits critically from its self-validation component.
paper_authors._id: 67847bca43f26c67cc29d68f
paper_authors.name: Zhengyang Tang
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paper_authors.name: Ziniu Li
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paper_authors.fullname: Ziniu Li
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paper_authors.name: Zhenyang Xiao
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paper_authors.fullname: xiaozhenyang
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paper_authors.name: Tian Ding
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paper_authors.name: Ruoyu Sun
paper_authors._id: 67847bca43f26c67cc29d694
paper_authors.name: Benyou Wang
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paper_authors.fullname: Wang
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paper_authors.user: Benyou
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paper_authors.name: Dayiheng Liu
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paper_authors.fullname: Dayiheng Liu
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paper_authors.user: Losin94
paper_authors._id: 67847bca43f26c67cc29d696
paper_authors.name: Fei Huang
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paper_authors.fullname: Fei Huang
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paper_authors.user: hzhwcmhf
paper_authors._id: 67847bca43f26c67cc29d697
paper_authors.name: Tianyu Liu
paper_authors._id: 67847bca43f26c67cc29d698
paper_authors.name: Bowen Yu
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paper_authors.fullname: Bowen Yu
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paper_authors._id: 67847bca43f26c67cc29d699
paper_authors.name: Junyang Lin
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paper_authors.fullname: Junyang Lin
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paper_authors.user: JustinLin610
paper_title: Enabling Scalable Oversight via Self-Evolving Critic
paper_summary: Despite their remarkable performance, the development of Large Language
  Models (LLMs) faces a critical challenge in scalable oversight: providing
  effective feedback for tasks where human evaluation is difficult or where LLMs
  outperform humans. While there is growing interest in using LLMs for critique,
  current approaches still rely on human annotations or more powerful models,
  leaving the issue of enhancing critique capabilities without external
  supervision unresolved. We introduce SCRIT (Self-evolving CRITic), a framework
  that enables genuine self-evolution of critique abilities. Technically, SCRIT
  self-improves by training on synthetic data, generated by a contrastive-based
  self-critic that uses reference solutions for step-by-step critique, and a
  self-validation mechanism that ensures critique quality through correction
  outcomes. Implemented with Qwen2.5-72B-Instruct, one of the most powerful LLMs,
  SCRIT achieves up to a 10.3\% improvement on critique-correction and error
  identification benchmarks. Our analysis reveals that SCRIT's performance scales
  positively with data and model size, outperforms alternative approaches, and
  benefits critically from its self-validation component.
paper_ai_summary: SCRIT, a self-evolving critique framework, enhances LLMs' critique capabilities using synthetic data and self-validation, achieving significant improvements in critique-correction and error identification benchmarks.
paper_ai_keywords: Large Language Models (LLMs) self-evolving synthetic data contrastive-based self-critic reference solutions step-by-step critique self-validation Qwen2.5-72B-Instruct critique-correction error identification benchmarks