Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight
As large language models become stronger, weak supervisors may fail to provide reliable labels, preferences, or final judgments for complex outputs, limiting both weak-to-strong generalization and scalable oversight. We study a more tractable form of weak supervision: using a weak model as a critic rather than as a labeler or judge. Instead of solving the task or selecting the correct answer, the weak critic only needs to provide a non-misleading revision direction that helps the strong model better use its own knowledge. We call this setting *weak-critic strong oversight*. We first show that weak critiques can improve frozen strong models at inference time, and that critique quality is k...
| Authors | Can Jin, Jiakang Li, Rui Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 May 2026 |
| Last updated at source | 21 August 2026 |
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