title: Efficient Inference for Large Reasoning Models: A Survey
summary: Large Reasoning Models (LRMs) significantly improve the reasoning ability of
  Large Language Models (LLMs) by learning to reason, exhibiting promising
  performance in complex task-solving. However, their deliberative reasoning
  process leads to inefficiencies in token usage, memory consumption, and
  inference time. Thus, this survey provides a review of efficient inference
  methods designed specifically for LRMs, focusing on mitigating token
  inefficiency while preserving the reasoning quality. First, we introduce a
  taxonomy to group the recent methods into two main categories: (a) explicit
  compact Chain-of-Thought (CoT), which reduces tokens while keeping the explicit
  reasoning structure, and (b) implicit latent CoT, which encodes reasoning steps
  within hidden representations instead of explicit tokens. Meanwhile, we discuss
  their strengths and weaknesses. Then, we conduct empirical analyses on existing
  methods from performance and efficiency aspects. Besides, we present open
  challenges in this field, including human-centric controllable reasoning,
  trade-off between interpretability and efficiency of reasoning, ensuring safety
  of efficient reasoning, and broader applications of efficient reasoning. In
  addition, we highlight key insights for enhancing LRMs' inference efficiency
  via techniques such as model merging, new architectures, and agent routers. We
  hope this work serves as a valuable guide, helping researchers overcome
  challenges in this vibrant
  fieldhttps://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs.
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paper_authors.name: Yue Liu
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paper_authors.name: Yufei He
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paper_authors.name: Baolong Bi
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paper_authors.fullname: Jiaheng Zhang
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paper_authors.name: Zhiqi Huang
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paper_authors.fullname: Zhiqi Huang
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paper_authors.user: Angelalilyer
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paper_authors.name: Bryan Hooi
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paper_title: Efficient Inference for Large Reasoning Models: A Survey
paper_summary: Large Reasoning Models (LRMs) significantly improve the reasoning ability of
  Large Language Models (LLMs) by learning to reason, exhibiting promising
  performance in complex task-solving. However, their deliberative reasoning
  process leads to inefficiencies in token usage, memory consumption, and
  inference time. Thus, this survey provides a review of efficient inference
  methods designed specifically for LRMs, focusing on mitigating token
  inefficiency while preserving the reasoning quality. First, we introduce a
  taxonomy to group the recent methods into two main categories: (a) explicit
  compact Chain-of-Thought (CoT), which reduces tokens while keeping the explicit
  reasoning structure, and (b) implicit latent CoT, which encodes reasoning steps
  within hidden representations instead of explicit tokens. Meanwhile, we discuss
  their strengths and weaknesses. Then, we conduct empirical analyses on existing
  methods from performance and efficiency aspects. Besides, we present open
  challenges in this field, including human-centric controllable reasoning,
  trade-off between interpretability and efficiency of reasoning, ensuring safety
  of efficient reasoning, and broader applications of efficient reasoning. In
  addition, we highlight key insights for enhancing LRMs' inference efficiency
  via techniques such as model merging, new architectures, and agent routers. We
  hope this work serves as a valuable guide, helping researchers overcome
  challenges in this vibrant
  fieldhttps://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs.
paper_ai_summary: This survey reviews efficient inference methods for Large Reasoning Models to reduce token usage and memory consumption while maintaining reasoning quality, categorizing methods into explicit compact Chain-of-Thought and implicit latent CoT.
paper_ai_keywords: Large Reasoning Models Large Language Models deliberative reasoning token usage memory consumption inference time efficient inference methods Chain-of-Thought latent CoT human-centric controllable reasoning interpretability safety model merging new architectures agent routers