Don't Overthink It: A Survey of Efficient R1-style Large Reasoning
  Models
Recently, Large Reasoning Models (LRMs) have gradually become a research
hotspot due to their outstanding performance in handling complex tasks. Among
them, DeepSeek R1 has garnered significant attention for its exceptional
performance and open-source nature, driving advancements in the research of
R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models
enhance logical deduction and decision-making capabilities during reasoning by
incorporating mechanisms such as long chain-of-thought and self-reflection
through reinforcement learning. However, with the widespread application of
these models, the problem of overthinking has gradually emerged. Specifically,
when generating answers, these models often construct excessively long
reasoning chains with redundant or repetitive steps, which leads to reduced
reasoning efficiency and may affect the accuracy of the final answer. To this
end, various efficient reasoning methods have been proposed, aiming to reduce
the length of reasoning paths without compromising model performance and
reasoning capability. By reviewing the current research advancements in the
field of efficient reasoning methods systematically, we categorize existing
works into two main directions based on the lens of single-model optimization
versus model collaboration: (1) Efficient Reasoning with Single Model, which
focuses on improving the reasoning efficiency of individual models; and (2)
Efficient Reasoning with Model Collaboration, which explores optimizing
reasoning paths through collaboration among multiple models. Besides, we
maintain a public GitHub repository that tracks the latest progress in
efficient reasoning methods.
68956be848b0ae5ca2710d9c
Linan Yue
68956be848b0ae5ca2710d9d
Yichao Du
claimed_verified
2025-08-08T16:19:32.141Z
63e1d3451e5a4f34b7a728ef
https://cdn-avatars.huggingface.co/v1/production/uploads/63e1d3451e5a4f34b7a728ef/kHE5JHF9iZJOG0uBkvJr-.jpeg
yichaodu
user
yichaodu
68956be848b0ae5ca2710d9e
Yizhi Wang
68956be848b0ae5ca2710d9f
Weibo Gao
68956be848b0ae5ca2710da0
Fangzhou Yao
68956be848b0ae5ca2710da1
Li Wang
68956be848b0ae5ca2710da2
Ye Liu
68956be848b0ae5ca2710da3
Ziyu Xu
68956be848b0ae5ca2710da4
Qi Liu
68956be848b0ae5ca2710da5
Shimin Di
68956be848b0ae5ca2710da6
Min-Ling Zhang
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning
  Models
Recently, Large Reasoning Models (LRMs) have gradually become a research
hotspot due to their outstanding performance in handling complex tasks. Among
them, DeepSeek R1 has garnered significant attention for its exceptional
performance and open-source nature, driving advancements in the research of
R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models
enhance logical deduction and decision-making capabilities during reasoning by
incorporating mechanisms such as long chain-of-thought and self-reflection
through reinforcement learning. However, with the widespread application of
these models, the problem of overthinking has gradually emerged. Specifically,
when generating answers, these models often construct excessively long
reasoning chains with redundant or repetitive steps, which leads to reduced
reasoning efficiency and may affect the accuracy of the final answer. To this
end, various efficient reasoning methods have been proposed, aiming to reduce
the length of reasoning paths without compromising model performance and
reasoning capability. By reviewing the current research advancements in the
field of efficient reasoning methods systematically, we categorize existing
works into two main directions based on the lens of single-model optimization
versus model collaboration: (1) Efficient Reasoning with Single Model, which
focuses on improving the reasoning efficiency of individual models; and (2)
Efficient Reasoning with Model Collaboration, which explores optimizing
reasoning paths through collaboration among multiple models. Besides, we
maintain a public GitHub repository that tracks the latest progress in
efficient reasoning methods.
Research on efficient reasoning methods for Large Reasoning Models (LRMs) aims to reduce reasoning path length without sacrificing performance, through single-model optimization and model collaboration.
Large Reasoning Models DeepSeek R1 Large Language Models long chain-of-thought self-reflection reinforcement learning efficient reasoning methods single-model optimization model collaboration