Why We Think

Why We Think#

来源: https://lilianweng.github.io/posts/2025-05-01-thinking/

摘要: 这篇文章讨论了测试时计算(test-time compute)和思维链(Chain-of-Thought, CoT)在人工智能模型性能提升中的重要作用。文章引用了多位研究者(包括Graves、Ling、Cobbe等)的工作,探讨了如何有效利用测试时的计算资源(即’思考时间’)以及其作用机制。思维链是近年来大语言模型领域的重要突破,它允许模型通过步骤分解和中间推理过程来解决复杂问题。文章旨在回顾这一领域的最新发展,分析为什么给予模型额外的’思考时间’能够提升其性能。这个主题与当前AI研究的核心问题密切相关,即如何提升模型的推理能力和问题解决能力。通过理解和优化模型的’思考’过程,研究者们希望开发

关键词: 测试时计算, 思维链, 模型推理, 计算资源优化, 人工智能性能

Why We Think#

This article discusses the significant role of test-time computation and Chain-of-Thought (CoT) in improving artificial intelligence model performance. Drawing from the work of various researchers (including Graves, Ling, Cobbe, and others), it explores how to effectively utilize computational resources during testing (i.e., ’thinking time’) and the mechanisms behind it. Chain-of-Thought represents a major breakthrough in the field of large language models in recent years, enabling models to solve complex problems through step-by-step decomposition and intermediate reasoning processes. The article aims to review the latest developments in this field and analyze why giving models additional ’thinking time’ can enhance their performance. This topic is closely related to core questions in current AI research, namely how to improve models’ reasoning abilities and problem-solving capabilities. Through understanding and optimizing models’ ’thinking’ processes, researchers hope to develop more effective AI systems.