提出推理算法理论框架，解释大模型如何迭代优化答案。
Algorithmic Thinking Theory
- 将多次生成与合并答案视为基于概率预言机的推理算法。
- 理论框架可统一解释主流迭代改进方法的有效性。
- 不依赖模型结构，适用于各类未来推理系统。
大型语言模型在解决复杂推理任务方面表现优异。令人意外的是，通过迭代优化先前生成的答案，其性能往往能得到提升。在此背景下，生成并组合一组解的推理计划可被视为一种使用概率预言机的推理算法。本文提出一个理论框架，用于分析此类推理算法。该框架形式化了当前流行的迭代改进与答案聚合技术的基本原则，为设计新一代更强的推理方法提供了基础。与依赖模型架构细节的理解方法不同，本框架基于实验证据，因此具有普遍性，可能适用于当前及未来的多种推理预言机。
原文摘要 · Abstract (English)
Large language models (LLMs) have proven to be highly effective for solving complex reasoning tasks. Surprisingly, their capabilities can often be improved by iterating on previously generated solutions. In this context, a reasoning plan for generating and combining a set of solutions can be thought of as an algorithm for reasoning using a probabilistic oracle. We introduce a theoretical framework for analyzing such reasoning algorithms. This framework formalizes the principles underlying popular techniques for iterative improvement and answer aggregation, providing a foundation for designing a new generation of more powerful reasoning methods. Unlike approaches for understanding models that rely on architectural specifics, our model is grounded in experimental evidence. As a result, it offers a general perspective that may extend to a wide range of current and future reasoning oracles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。