为大模型推理提供快慢结合、内外协同的新分类方法。
Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
- 按直觉与深思、内部参数与外部工具划分推理策略
- 系统梳理自适应推理研究，归纳关键决策因素
- 适合关注大模型推理效率与可靠性的研究人员
大语言模型在多个领域展现出卓越的推理能力。然而，在真实任务中有效推理需要根据问题需求调整策略，从快速直觉响应到逐步深思，再到借助外部工具的增强推理。受认知心理学启发，我们提出一种新的大模型推理策略分类体系，基于两个知识边界：快/慢边界（区分直觉与深思过程），以及内/外边界（区分模型参数内生推理与外部工具增强推理）。我们系统综述了近期关于大模型自适应推理的研究工作，并依据关键决策因素对方法进行分类。最后，我们指出当前开放挑战与未来方向，旨在推动更自适应、高效、可靠的大型语言模型发展。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reasoning strategy to the demands of the problem, ranging from fast, intuitive responses to deliberate, step-by-step reasoning and tool-augmented thinking. Drawing inspiration from cognitive psychology, we propose a novel taxonomy of LLM reasoning strategies along two knowledge boundaries: a fast/slow boundary separating intuitive from deliberative processes, and an internal/external boundary distinguishing reasoning grounded in the model's parameters from reasoning augmented by external tools. We systematically survey recent work on adaptive reasoning in LLMs and categorize methods based on key decision factors. We conclude by highlighting open challenges and future directions toward more adaptive, efficient, and reliable LLMs.
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