A Survey of Efficient Reasoning for Large Reasoning Models: Language,
  Multimodality, and Beyond
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have
demonstrated strong performance gains by scaling up the length of
Chain-of-Thought (CoT) reasoning during inference. However, a growing concern
lies in their tendency to produce excessively long reasoning traces, which are
often filled with redundant content (e.g., repeated definitions), over-analysis
of simple problems, and superficial exploration of multiple reasoning paths for
harder tasks. This inefficiency introduces significant challenges for training,
inference, and real-world deployment (e.g., in agent-based systems), where
token economy is critical. In this survey, we provide a comprehensive overview
of recent efforts aimed at improving reasoning efficiency in LRMs, with a
particular focus on the unique challenges that arise in this new paradigm. We
identify common patterns of inefficiency, examine methods proposed across the
LRM lifecycle, i.e., from pretraining to inference, and discuss promising
future directions for research. To support ongoing development, we also
maintain a real-time GitHub repository tracking recent progress in the field.
We hope this survey serves as a foundation for further exploration and inspires
innovation in this rapidly evolving area.
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Xiaoye Qu
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Yafu Li
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Yafu Li
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Zhaochen Su
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Weigao Sun
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Weigao Sun
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Jianhao Yan
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Jianhao Yan
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Dongrui Liu
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Dongrui Liu
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Ganqu Cui
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Ganqu Cui
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Daizong Liu
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Shuxian Liang
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Shuxian Liang
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Junxian He
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Junxian He
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Peng Li
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Wei Wei
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Jing Shao
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Chaochao Lu
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Yue Zhang
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Xian-Sheng Hua
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Bowen Zhou
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BowenZhou
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Yu Cheng
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Yu Cheng
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A Survey of Efficient Reasoning for Large Reasoning Models: Language,
  Multimodality, and Beyond
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have
demonstrated strong performance gains by scaling up the length of
Chain-of-Thought (CoT) reasoning during inference. However, a growing concern
lies in their tendency to produce excessively long reasoning traces, which are
often filled with redundant content (e.g., repeated definitions), over-analysis
of simple problems, and superficial exploration of multiple reasoning paths for
harder tasks. This inefficiency introduces significant challenges for training,
inference, and real-world deployment (e.g., in agent-based systems), where
token economy is critical. In this survey, we provide a comprehensive overview
of recent efforts aimed at improving reasoning efficiency in LRMs, with a
particular focus on the unique challenges that arise in this new paradigm. We
identify common patterns of inefficiency, examine methods proposed across the
LRM lifecycle, i.e., from pretraining to inference, and discuss promising
future directions for research. To support ongoing development, we also
maintain a real-time GitHub repository tracking recent progress in the field.
We hope this survey serves as a foundation for further exploration and inspires
innovation in this rapidly evolving area.
The survey focuses on addressing inefficiencies in reasoning efficiency of Large Reasoning Models (LRMs) by examining methods across their lifecycle and identifying common inefficiency patterns.
Large Reasoning Models DeepSeek-R1 OpenAI o1 Chain-of-Thought reasoning traces redundant content token economy