Efficient Inference for Large Reasoning Models: A Survey
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.
67eb58c71e23a7499b683cce
Yue Liu
admin_assigned
2025-04-01T08:11:01.812Z
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yueliu1999
user
yueliu1999
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Jiaying Wu
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Yufei He
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Hongcheng Gao
admin_assigned
2025-04-01T08:11:54.944Z
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Hongcheng Gao
user
HongchengGao
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Hongyu Chen
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2025-04-01T08:12:05.159Z
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Hongyu Chen
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HongyuChen
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Baolong Bi
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2025-04-01T08:12:18.251Z
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Bibaolong
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Bibaolong
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Jiaheng Zhang
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2025-04-01T08:12:23.776Z
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Jiaheng Zhang
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jiaheng233
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Zhiqi Huang
admin_assigned
2025-04-01T08:12:36.398Z
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Zhiqi Huang
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Angelalilyer
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Bryan Hooi
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2025-04-01T08:12:42.880Z
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Bryan Hooi
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bhooi
Efficient Inference for Large Reasoning Models: A Survey
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.
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.
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