From System 1 to System 2: A Survey of Reasoning Large Language Models
Zhang, Duzhen Li, Zhongzhi Zhang, Mingliang Zhang, Jiaxin Liu, Zengyan Yao, Yuxuan Xu, Haotian Zheng, Junhao Chen, Xiuyi Zhang, Yingying
Zhang, Duzhen
Li, Zhongzhi
Zhang, Mingliang
Zhang, Jiaxin
Liu, Zengyan
Yao, Yuxuan
Xu, Haotian
Zheng, Junhao
Chen, Xiuyi
Zhang, Yingying
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundational Large Language Models (LLMs) excel at fast decision-making but lack the depth for complex reasoning, as they have not yet fully embraced the step-by-step analysis characteristic of true System 2 thinking. Recently, reasoning LLMs like OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding, closely mimicking the deliberate reasoning of System 2 and showcasing human- like cognitive abilities. This survey begins with a brief overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs. Next, we discuss how to construct reasoning LLMs, trace the evolution of various reasoning models, and examine the core methods that enable advanced reasoning behind them. Additionally, we provide an overview of reasoning benchmarks, offering an in-depth comparison of the performance of representative reasoning LLMs. Finally, we explore promising directions for advancing reasoning LLMs and maintain a real-time GitHub Repository to track the latest developments. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this rapidly evolving field.
Citation
D. Zhang et al., "From System 1 to System 2: A Survey of Reasoning Large Language Models," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2025.3637037
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conference
Keywords
advanced AI architectures, AGI, decision making in AI, human- like cognitive abilities, human- like reasoning, large language models, slow-thinking, system 2 reasoning
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Source
Publisher
IEEE