When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning

• URL: http://arxiv.org/abs/2604.06787v1
• Date: Wed, 08 Apr 2026 07:56:28 GMT
• Title: When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
• Abstract summary: We introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning.<n>Inspired by human metacognition, DTSR operates in two stages: Reflection Signal Monitoring, and Thought Sufficiency Check.<n>DTSR reduces reasoning length by 28.9%-34.9% with minimal performance loss, effectively mitigating overthinking.
• Score: 52.21239821135325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large reasoning models (LRMs) have achieved remarkable performance in complex reasoning tasks, driven by their powerful inference-time scaling capability. However, LRMs often suffer from overthinking, which results in substantial computational redundancy and significantly reduces efficiency. Early-exit methods aim to mitigate this issue by terminating reasoning once sufficient evidence has been generated, yet existing approaches mostly rely on handcrafted or empirical indicators that are unreliable and impractical. In this work, we introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning that enables the model to dynamically assess the sufficiency of its chain-of-thought (CoT) and determine the optimal point for early exit. Inspired by human metacognition, DTSR operates in two stages: (1) Reflection Signal Monitoring, which identifies reflection signals as potential cues for early exit, and (2) Thought Sufficiency Check, which evaluates whether the current CoT is sufficient to derive the final answer. Experimental results on the Qwen3 models show that DTSR reduces reasoning length by 28.9%-34.9% with minimal performance loss, effectively mitigating overthinking. We further discuss overconfidence in LRMs and self-evaluation paradigms, providing valuable insights for early-exit reasoning.

Related papers

• Efficient Reasoning with Balanced Thinking [31.690456174428068]
  Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities.<n>LRMs often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking.<n>We propose ReBalance, a training-free framework that achieves efficient reasoning with balanced thinking.
  arXiv  Detail & Related papers  (2026-03-12T18:48:07Z)
• Does Your Reasoning Model Implicitly Know When to Stop Thinking? [45.954548163594204]
  We show that LRMs implicitly know the appropriate time to stop thinking, while this capability is obscured by current sampling paradigms.<n>Motivated by this, we introduce SAGE, a novel sampling paradigm that unleashes this efficient reasoning potential.
  arXiv  Detail & Related papers  (2026-02-09T07:38:22Z)
• Stop Spinning Wheels: Mitigating LLM Overthinking via Mining Patterns for Early Reasoning Exit [114.83867400179354]
  Overthinking can degrade overall performance of large language models.<n>We categorize reasoning into three stages: insufficient exploration stage, compensatory reasoning stage, and reasoning convergence stage.<n>We develop a lightweight thresholding strategy based on rules to improve reasoning accuracy.
  arXiv  Detail & Related papers  (2025-08-25T03:17:17Z)
• Promoting Efficient Reasoning with Verifiable Stepwise Reward [7.385337642642193]
  Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning.<n>LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency.<n>We propose a novel rule-based verifiable stepwise reward mechanism (VSRM), which assigns rewards based on the performance of intermediate states in the reasoning trajectory.
  arXiv  Detail & Related papers  (2025-08-14T02:43:53Z)
• Lost at the Beginning of Reasoning [85.17612793300238]
  We show that the first reasoning step exerts a disproportionately large influence on the final prediction.<n>We propose an efficient sampling strategy that leverages a reward model to identify and retain high-quality first reasoning steps.
  arXiv  Detail & Related papers  (2025-06-27T09:53:57Z)
• Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs [8.359909829007005]
  We investigate whether efficient reasoning strategies introduce behavioral inconsistencies in large reasoning models (LRMs)<n>$ICBENCH$ is a benchmark designed to measure inconsistency in LRMs across three dimensions.<n>We find that while larger models generally exhibit greater consistency than smaller ones, they all display widespread "scheming" behaviors.
  arXiv  Detail & Related papers  (2025-06-24T10:25:28Z)
• Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement [101.77467538102924]
  Large reasoning models (LRMs) exhibit overthinking, which hinders efficiency and inflates inference cost.<n>We propose two lightweight methods to enhance LRM efficiency.<n>First, we introduce Efficiency Steering, a training-free activation steering technique that modulates reasoning behavior via a single direction.<n>Second, we develop Self-Rewarded Efficiency RL, a reinforcement learning framework that dynamically balances task accuracy and brevity.
  arXiv  Detail & Related papers  (2025-06-18T17:18:12Z)
• When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning [19.329523111916682]
  Large reasoning models (LRMs) have significantly advanced performance on complex tasks, yet their tendency to overthink introduces inefficiencies.<n>This study investigates the internal mechanisms of reinforcement learning (RL)-trained LRMs when prompted to save thinking.
  arXiv  Detail & Related papers  (2025-05-21T08:55:35Z)
• ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [64.93140713419561]
  Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs.<n>Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection.<n>We introduce ConCISE, a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, and Early Stopping to terminate reasoning when confidence is sufficient.
  arXiv  Detail & Related papers  (2025-05-08T01:40:40Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.