Decoding the Critique Mechanism in Large Reasoning Models

arXiv cs.LG / 3/18/2026

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Key Points

Large Reasoning Models exhibit backtracking and self-verification, and the paper argues that strong critique ability is needed to detect errors and trigger self-correction.
By deliberately inserting arithmetic mistakes into intermediate reasoning steps, the study shows that models can still arrive at correct final answers, revealing an internal hidden critique mechanism.
The authors identify a highly interpretable 'critique vector' in latent space and demonstrate that steering representations along this vector improves error detection without additional training.
Experiments across multiple model scales and families suggest the critique mechanism is robust and can be exploited to improve self-verification and test-time scaling.
The authors provide code at https://github.com/mail-research/lrm-critique-vectors to reproduce and extend their results.

Abstract

Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothesize that such behaviors are beneficial only when the model has sufficiently strong "critique" ability to detect its own mistakes. This work systematically investigates how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps. Notably, we discover a peculiar yet important phenomenon: despite the error propagating through the chain-of-thought (CoT), resulting in an incorrect intermediate conclusion, the model still reaches the correct final answer. This recovery implies that the model must possess an internal mechanism to detect errors and trigger self-correction, which we refer to as the hidden critique ability. Building on feature space analysis, we identify a highly interpretable critique vector representing this behavior. Extensive experiments across multiple model scales and families demonstrate that steering latent representations with this vector improves the model's error detection capability and enhances the performance of test-time scaling at no extra training cost. Our findings provide a valuable understanding of LRMs' critique behavior, suggesting a promising direction to control and improve their self-verification mechanism. Our code is available at https://github.com/mail-research/lrm-critique-vectors.