Towards Structured Reasoning: A Comprehensive Survey of Chain-of-Thought Reasoning in Large Language Models

Yang Mo, Enze Ge, Xudong Han, Junhao Song, Ziqian Bi, Tianyang Wang, Yanliang Li, Binhua Huang

Abstract: Chain-of-Thought (CoT) reasoning has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). This survey provides a comprehensive analysis of CoT reasoning, spanning from foundational prompting techniques to the recent emergence of Large Reasoning Models (LRMs) such as OpenAI o1 and DeepSeek-R1. We present a unified taxonomy that categorizes CoT methods into four dimensions: (1) prompting strategies including zero-shot, few-shot, and self-consistency approaches; (2) structural extensions such as Tree-of-Thought, Graph-of-Thought, and Forestof-Thought; (3) training paradigms encompassing supervised fine-tuning, reinforcement learning, and preference optimization; and (4) emerging paradigms including latent reasoning and multi-modal CoT. Through extensive analysis of over 150 papers, we identify key characteristics that distinguish Long CoT reasoning, including deep multi-step inference, heuristicguided search, and self-verification mechanisms. We systematically compare performance across 25+ benchmarks spanning mathematical, commonsense, symbolic, logical, and multi-modal reasoning tasks. Our analysis reveals critical challenges including reasoning faithfulness, computational efficiency, and the "illusion of thinking" phenomenon. We conclude by outlining promising research directions including efficient reasoning, multi-modal integration, and the development of more robust evaluation frameworks. This survey serves as a comprehensive resource for researchers and practitioners seeking to understand and advance the frontier of reasoning in LLMs.

External IDs:doi:10.36227/techrxiv.176617594.46953319/v1