arXiv cs.AI· Quang Hung Nguyen, Hanh Nguyen, Thanh Tam Nguyen, Quoc Viet Hung Nguyen, Trinh Pham, Thanh Trung Huynh·· 3 小时前AI 评分42
大型推理模型早期停止方法综述与实证审计：效率与可靠性
Early Stopping in Large Reasoning Models: A Survey and Empirical Audit of Efficiency and Reliability
AI 导读
arXiv 论文对大型推理模型的早期停止（early-stopping）方法进行综述与实证审计，通过统一的 rollout 重放协议在五个推理模型上评估数学、科学推理、代码生成和事实性任务，并提出 Target-Integrated Token Efficiency（TITE）衡量相对 oracle 的效率。
正文 · 原文
Abstract:Large reasoning models can improve accuracy by generating longer reasoning traces, but some of this computation may be unnecessary. Early-stopping methods aim to stop once enough reasoning is available to answer. Evaluating these methods requires accounting for the cost of deciding when to stop and the reliability of the resulting answer. We evaluate different methods across five reasoning models on mathematical and scientific reasoning, code generation, and factuality tasks by using a shared rollout replay protocol. We introduce Target-Integrated Token Efficiency (TITE) to measure efficiency relative to the oracle across accuracy targets. We also examine correctness at later exit positions, transfer across models and tasks, post-stop reasoning continuation, and retained justification quality. In our experiments, correctness can decrease at later positions beyond what the fitted decoding-noise model predicts. Reliability checks also reveal continued reasoning after forced termination and weaker supporting reasoning in some correct early-stopping outputs. The implementation can be found in this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.00166 [cs.AI] |
| (or arXiv:2611.00166v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00166 arXiv-issued DOI via DataCite |
Submission history
From: Hung Nguyen Quang [view email]
[v1]
Fri, 30 Oct 2026 11:39:08 UTC (2,336 KB)
[v2]
Thu, 12 Nov 2026 13:12:13 UTC (2,263 KB)
来源：arXiv cs.AI · arxiv.org