Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Authors: Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, Ed Chi (2022)

arXiv: 2205.10625

Domains

Reasoning

TLDR (English)

"Break hard problems into easy ones, solve sequentially" is another reasoning paradigm parallel to CoT, especially effective for compositional generalization. Together with CoT/ToT forms the trio of "how to guide LLM step-by-step thinking".

TLDR（中文）

"先把难题拆成易题，再依次解决"是另一条与 CoT 平行的推理范式，对组合泛化任务尤其有效。和 CoT/ToT 一起构成"如何引导大模型分步思考"的三件套。

Appears in These Articles

• 提示工程：与模型对话的艺术
• Prompt Engineering: The Art of Talking to Models

Co-cited Papers

These papers appear in the same articles as this one

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning DeepSeek-AI (2025)
• Large Language Models are Zero-Shot Reasoners Takeshi Kojima et al. (2022)
• Let's Verify Step by Step Hunter Lightman et al. (2023)
• Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang et al. (2022)

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