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Large language models (LLMs) have dramatically enhanced the field of languageintelligence, as demonstrably evidenced by their formidable empiricalperformance across a spectrum of complex reasoning tasks. Additionally,theoretical proofs have illuminated their emergent reasoning capabilities,providing a compelling showcase of their advanced cognitive abilities inlinguistic contexts. Critical to their remarkable efficacy in handling complexreasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoningtechniques, obliging them to formulate intermediate steps en route to derivingan answer. The CoT reasoning approach has not only exhibited proficiency inamplifying reasoning performance but also in enhancing interpretability,controllability, and flexibility. In light of these merits, recent researchendeavors have extended CoT reasoning methodologies to nurture the developmentof autonomous language agents, which adeptly adhere to language instructionsand execute actions within varied environments. This survey paper orchestratesa thorough discourse, penetrating vital research dimensions, encompassing: (i)the foundational mechanics of CoT techniques, with a focus on elucidating thecircumstances and justification behind its efficacy; (ii) the paradigm shift inCoT; and (iii) the burgeoning of language agents fortified by CoT approaches.Prospective research avenues envelop explorations into generalization,efficiency, customization, scaling, and safety. This paper caters to a wideaudience, including beginners seeking comprehensive knowledge of CoT reasoningand language agents, as well as experienced researchers interested infoundational mechanics and engaging in cutting-edge discussions on thesetopics. A repository for the related papers is available athttps://github.com/Zoeyyao27/CoT-Igniting-Agent.

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents