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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2505.02665 |
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| _version_ | 1866908353491369984 |
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| author | Pan, Qianjun Ji, Wenkai Ding, Yuyang Li, Junsong Chen, Shilian Wang, Junyi Zhou, Jie Chen, Qin Zhang, Min Wu, Yulan He, Liang |
| author_facet | Pan, Qianjun Ji, Wenkai Ding, Yuyang Li, Junsong Chen, Shilian Wang, Junyi Zhou, Jie Chen, Qin Zhang, Min Wu, Yulan He, Liang |
| contents | This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic "slow thinking" - a reasoning process inspired by human cognition, as described in Kahneman's Thinking, Fast and Slow. These models, like OpenAI's o1, focus on scaling computational resources dynamically during complex tasks, such as math reasoning, visual reasoning, medical diagnosis, and multi-agent debates. We present the development of reasoning LLMs and list their key technologies. By synthesizing over 100 studies, it charts a path toward LLMs that combine human-like deep thinking with scalable efficiency for reasoning. The review breaks down methods into three categories: (1) test-time scaling dynamically adjusts computation based on task complexity via search and sampling, dynamic verification; (2) reinforced learning refines decision-making through iterative improvement leveraging policy networks, reward models, and self-evolution strategies; and (3) slow-thinking frameworks (e.g., long CoT, hierarchical processes) that structure problem-solving with manageable steps. The survey highlights the challenges and further directions of this domain. Understanding and advancing the reasoning abilities of LLMs is crucial for unlocking their full potential in real-world applications, from scientific discovery to decision support systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law Pan, Qianjun Ji, Wenkai Ding, Yuyang Li, Junsong Chen, Shilian Wang, Junyi Zhou, Jie Chen, Qin Zhang, Min Wu, Yulan He, Liang Artificial Intelligence This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic "slow thinking" - a reasoning process inspired by human cognition, as described in Kahneman's Thinking, Fast and Slow. These models, like OpenAI's o1, focus on scaling computational resources dynamically during complex tasks, such as math reasoning, visual reasoning, medical diagnosis, and multi-agent debates. We present the development of reasoning LLMs and list their key technologies. By synthesizing over 100 studies, it charts a path toward LLMs that combine human-like deep thinking with scalable efficiency for reasoning. The review breaks down methods into three categories: (1) test-time scaling dynamically adjusts computation based on task complexity via search and sampling, dynamic verification; (2) reinforced learning refines decision-making through iterative improvement leveraging policy networks, reward models, and self-evolution strategies; and (3) slow-thinking frameworks (e.g., long CoT, hierarchical processes) that structure problem-solving with manageable steps. The survey highlights the challenges and further directions of this domain. Understanding and advancing the reasoning abilities of LLMs is crucial for unlocking their full potential in real-world applications, from scientific discovery to decision support systems. |
| title | A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.02665 |