---
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canonical_url: "https://aidr.today/ad6788a6?lang=en"
title: "Early Stopping in Large Reasoning Models: A Survey and Empirical Audit of Efficiency and Reliability"
lang: "en"
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available_langs: ["en","vi"]
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published_at: "2026-11-03T05:00:00.000Z"
category: "Research"
topics: ["inference"]
source_urls: ["https://arxiv.org/abs/2611.00166"]
summary: "Large reasoning models can improve accuracy by generating longer reasoning traces, but some computation may be unnecessary. Early-stopping methods aim to stop once enough reasoning is available to answer. Target-Integrated Token Efficiency (TITE) is introduced to measure efficiency relative to the oracle across accuracy targets."
---

# Early Stopping in Large Reasoning Models: A Survey and Empirical Audit of Efficiency and Reliability

> [Open the canonical story](<https://aidr.today/ad6788a6?lang=en>)

**Published:** 2026-11-03T05:00:00.000Z
**Category:** Research
**Topics:** inference

## Summary

Large reasoning models can improve accuracy by generating longer reasoning traces, but some computation may be unnecessary\. Early\-stopping methods aim to stop once enough reasoning is available to answer\. Target\-Integrated Token Efficiency \(TITE\) is introduced to measure efficiency relative to the oracle across accuracy targets\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.00166>)