ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning

Abstract

Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs, increasing computational overhead. Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection, which fails to remove redundant content thoroughly. To address these limitations, this work begins by framing two key patterns of redundant reflection in LRMs—\textit{Confidence Deficit}, wherein the model reflects on correct intermediate steps, and \textit{Termination Delay}, where reflection continues after a verified, confident answer—through a confidence-guided perspective. Based on this, we introduce \textbf{ConCISE} (\textbf{Con}fidence-guided \textbf{C}ompression \textbf{I}n \textbf{S}tep-by-step \textbf{E}fficient Reasoning), a framework designed to generate concise reasoning chains, integrating \textit{Confidence Injection} to boost reasoning confidence, and \textit{Early Stopping} to terminate reasoning when confidence is sufficient. Extensive experiments demonstrate that compared to baseline methods, fine-tuning LRMs on \textbf{ConCISE}-generated data yields a better balance between compression and task performance, reducing length by up to ～50% under SimPO, while maintaining high task accuracy.