Stepwise Think-Critique: A Unified Framework for Robust and Interpretable LLM Reasoning

• URL: http://arxiv.org/abs/2512.15662v1
• Date: Wed, 17 Dec 2025 18:15:17 GMT
• Title: Stepwise Think-Critique: A Unified Framework for Robust and Interpretable LLM Reasoning
• Authors: Jiaqi Xu, Cuiling Lan, Xuejin Chen, Yan LU,
• Abstract summary: We propose Stepwise Think-Critique, a unified framework that interleaves reasoning and self-critique at each step within a single model.<n> STC is trained with a hybrid reinforcement learning objective combining reasoning rewards and critique-consistency rewards to jointly optimize reasoning quality and self-evaluation.
• Score: 47.867294403474176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions. However, most existing large language models (LLMs) decouple reasoning from verification: they either generate reasoning without explicit self-checking or rely on external verifiers to detect errors post hoc. The former lacks immediate feedback, while the latter increases system complexity and hinders synchronized learning. Motivated by human critical thinking, we propose Stepwise Think-Critique (STC), a unified framework that interleaves reasoning and self-critique at each step within a single model. STC is trained with a hybrid reinforcement learning objective combining reasoning rewards and critique-consistency rewards to jointly optimize reasoning quality and self-evaluation. Experiments on mathematical reasoning benchmarks show that STC demonstrates strong critic-thinking capabilities and produces more interpretable reasoning traces, representing a step toward LLMs with built-in critical thinking.

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