Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with reflection. Through a large-scale empirical analysis, we find that a substantial fraction of reflective steps consist of self-verification (recheck) that repeatedly confirm intermediate results. These rechecks occur frequently across models and benchmarks, yet the vast majority are confirmatory rather than corrective, rarely identifying errors and altering reasoning outcomes. This reveals a mismatch between how often self-verification is activated and how often it is actually useful. Motivated by this, we propose a novel, experience-driven test-time framework that reduces the overused verification. Our method detects the activation of recheck behavior, consults an offline experience pool of past verification outcomes, and estimates whether a recheck is likely unnecessary via efficient retrieval. When historical experience suggests unnecessary, a suppression signal redirects the model to proceed. Across multiple model and benchmarks, our approach reduces token usage up to 20.3% while maintaining the accuracy, and in some datasets even yields accuracy improvements.

翻译：大型推理模型通过生成带有反思的长推理轨迹来实现强大性能。通过大规模实证分析，我们发现反思步骤中有相当大比例由重复确认中间结果的自验证（再检查）构成。这些再检查在不同模型和基准测试中频繁出现，但绝大多数是确认性而非纠正性的，很少识别错误或改变推理结果。这揭示了自验证的激活频率与其实际效用之间的不匹配。受此启发，我们提出了一种新颖的、基于经验的测试时框架来减少过度验证。我们的方法检测再检查行为的激活，查询离线经验池中过往验证结果，并通过高效检索评估再检查是否可能不必要。当历史经验表明不必要时，抑制信号会引导模型继续执行。在多个模型和基准测试中，我们的方法在保持准确性的同时将标记使用量降低达20.3%，在某些数据集上甚至实现了准确率提升。