English(EN) How can an LLM switch between low-, medium-, and high-effort reasoning? And how does an LLM learn to reason more or less?

Sebastian Raschka 解释大型语言模型推理强度级别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-18 13:28

Sebastian Raschka 发表了一篇文章，详细介绍了大型语言模型（LLMs）在推理和训练过程中如何管理不同级别的推理强度。文章探讨了大型语言模型在低、中、高推理过程之间切换的机制。它还讨论了这些模型如何学会随着时间的推移调整其推理能力。 AI

影响提供了对大型语言模型内部工作原理的见解，可能有助于开发人员理解和优化模型性能。

排序理由该条目是由一位知名研究人员对大型语言模型推理机制的解释，而非主要发布或重要的行业事件。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 该条目是由一位知名研究人员对大型语言模型推理机制的解释，而非主要发布或重要的行业事件。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 40 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

X — Sebastian Raschka TIER_1 English(EN) · rasbt · 2026-07-18 13:28

大型语言模型如何切换低、中、高强度推理？大型语言模型又是如何学会进行更多或更少推理的？

How can an LLM switch between low-, medium-, and high-effort reasoning? And how does an LLM learn to reason more or less? I put together a “little” article explaining how these effort levels are implemented at inference time and during training. https://t.co/mc4qiCnq0C