Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models

2026-05-17

AI Summary

This work addresses the inefficiency of large reasoning models, which often waste computational resources and incur increased latency due to over-reasoning during chain-of-thought generation. To mitigate this, the authors propose PUMA, a novel framework that introduces semantic redundancy between reasoning steps as an early-stopping signal, complemented by answer-level verification to preserve semantic fidelity. PUMA features a lightweight, plug-and-play architecture compatible with diverse models and tasks. Experimental results across five prominent large reasoning models and five benchmarks demonstrate that PUMA reduces inference tokens by 26.2% on average while maintaining both answer accuracy and chain-of-thought quality.

Abstract

Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabilized and thereby wasting tokens and increasing latency. Existing inference-time early-exit methods rely primarily on answer-level signals, such as confidence or trial-answer consistency, to decide when to stop. However, these signals mainly reflect answer readiness rather than reasoning convergence: they may trigger before the model has finished exploring or self-correcting, causing premature exits that can degrade final-answer accuracy and leave the retained reasoning chain semantically incomplete. We identify reasoning-level semantic redundancy as a complementary signal for semantic-preserving early exit: when successive steps no longer add novel progress and instead revisit established conclusions, the reasoning trajectory has likely converged. Building on this insight, we propose PUMA, a plug-and-play framework that combines a lightweight Redundancy Detector with answer-level verification. The detector flags semantically redundant candidate exits, while verification confirms whether stopping is safe, allowing PUMA to remove redundant continuation while preserving both answer accuracy and a coherent reasoning prefix. Across five LRMs and five challenging reasoning benchmarks, PUMA achieves 26.2% average token reduction while preserving accuracy and retained CoT quality. Additional experiments on code generation, zero-shot vision-language reasoning, and learned stopping-policy internalization further demonstrate that reasoning-level redundancy is a robust, transferable, and learnable signal for efficient reasoning. Our code is available at \url{https://github.com/giovanni-vaccarino/PUMA}.

Problem

• early exit
• reasoning convergence
• semantic redundancy
• chain-of-thought
• Large Reasoning Models

Innovation

• early exit
• reasoning convergence
• semantic redundancy
• chain-of-thought
• large reasoning models