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2025

Efficient Inference for Large Reasoning Models: A Survey

Liu, Yue, Wu, Jiaying, He, Yufei et al.

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Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in solving complex tasks.

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.
The overview structure of this paper is shown in Figure~\ref{fig:paper_structure}.

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W. Zhao, J. Guo, Y. Deng, X. Sui, Y. Hu, Y. Zhao, W. Che, B. Qin, T.-S. Chua, and T. Liu, “Exploring and exploiting the inherent efficiency within large reasoning models for self-guided efficiency enhancement,” arXiv preprint arXiv:2506.15647 , 2025

2025

Closest in time.

X. Wan, W. Wang, W. Xu, W. Yin, J. Song, and M. Sun, “Adapthink: Adaptive thinking preferences for reasoning language model,” arXiv preprint arXiv:2506.18237 , 2025

2025

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B. Ding, Y. Chen, F. Wang, L. Ming, and T. Lin, “Do thinking tokens help or trap? towards more efficient large reasoning model,” 2025

2025

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R. Li, Z. Luo, Q. Zhang, R. Li, B. Zhou, A. Payani, and X. Du, “Aalc: Large language model efficient reasoning via adaptive accuracy-length control,” arXiv preprint arXiv:2506.20160 , 2025

2025

Closest in time.

Z. Zhang, X. He, W. Yan, A. Shen, C. Zhao, S. Wang, Y. Shen, and X. E. Wang, “Soft thinking: Unlocking the reasoning potential of llms in continuous concept space,” arXiv preprint arXiv:2505.15778 , 2025

2025

Closest in time.

Z. Shen, H. Yan, L. Zhang, Z. Hu, Y. Du, and Y. He, “Codi: Compressing chain-of-thought into continuous space via self-distillation,” arXiv preprint arXiv:2502.21074 , 2025

2025

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X. Shen, Y. Wang, X. Shi, Y. Wang, P. Zhao, and J. Gu, “Efficient reasoning with hidden thinking,” arXiv preprint arXiv:2501.19201 , 2025

2025

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D. Su, H. Zhu, Y. Xu, J. Jiao, Y. Tian, and Q. Zheng, “Token assorted: Mixing latent and text tokens for improved language model reasoning,” arXiv preprint arXiv:2502.03275 , 2025

2025

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Y. Xu, X. Guo, Z. Zeng, and C. Miao, “Softcot: Soft chain-of-thought for efficient reasoning with llms,” arXiv preprint arXiv:2502.12134 , 2025

2025

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W. Tan, J. Li, J. Ju, Z. Luo, J. Luan, and R. Song, “Think silently, think fast: Dynamic latent compression of llm reasoning chains,” arXiv preprint arXiv:2505.16552v3 , 2025

Original

2025

Closest in time.

X. Wang, D. Wang, W. Ying, H. Bai, N. Gong, S. Dong, K. Liu, and Y. Fu, “Efficient post-training refinement of latent reasoning in large language models,” arXiv preprint arXiv:2506.08552 , 2025

2025

Closest in time.

N. Jiang, Z. Wu, D.-C. Zhan, F. Lai, and S. Lian, “Dart: Distilling autoregressive reasoning to silent thought,” arXiv preprint arXiv:2506.11752 , 2025

2025

Closest in time.

Y. He, Y. Li, J. Wu, Y. Sui, Y. Chen, and B. Hooi, “Evaluating the paperclip maximizer: Are rl-based language models more likely to pursue instrumental goals?” arXiv preprint arXiv:2502.12206 , 2025

2025

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C. Wang, Y. Liu, B. Li, D. Zhang, Z. Li, and J. Fang, “Safety in large reasoning models: A survey,” arXiv preprint arXiv:2504.17704 , 2025

2025

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OpenAI, “Detecting misbehavior in frontier reasoning models,” https://openai.com/index/chain-of-thought-monitoring/ , 2025

2025

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Y. Liu, H. Gao, S. Zhai, X. Jun, T. Wu, Z. Xue, Y. Chen, K. Kawaguchi, J. Zhang, and B. Hooi, “Guardreasoner: Towards reasoning-based llm safeguards,” arXiv preprint arXiv:2501.18492 , 2025

2025

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Y. Liu, S. Zhai, M. Du, Y. Chen, T. Cao, H. Gao, C. Wang, X. Li, K. Wang, J. Fang, J. Zhang, and B. Hooi, “Guardreasoner-vl: Safeguarding vlms via reinforced reasoning,” arXiv preprint arXiv:2505.11049 , 2025

2025

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R. Gong, Y. Liu, W. Qu, M. Du, Y. He, Y. Ma, Y. Chen, X. Liu, Y. Wen, X. Li et al. , “Efficient reasoning via chain of unconscious thought,” arXiv preprint arXiv:2505.19756 , 2025

2025

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S. Thapa, S. Shiwakoti, S. B. Shah, S. Adhikari, H. Veeramani, M. Nasim, and U. Naseem, “Large language models (llm) in computational social science: prospects, current state, and challenges,” Social Network Analysis and Mining , pp. 1–30, 2025

2025

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S. Wu, Y. Deng, Y. Zhu, W. Hsu, and M. L. Lee, “From personas to talks: Revisiting the impact of personas on llm-synthesized emotional support conversations,” arXiv preprint arXiv:2502.11451 , 2025

2025

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OpenAI, “Introducing gpt-4.5,” https://openai.com/index/introducing-gpt-4-5/ , 2025

2025

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Y. Ji, H. Tan, J. Shi, X. Hao, Y. Zhang, H. Zhang, P. Wang, M. Zhao, Y. Mu, P. An et al. , “Robobrain: A unified brain model for robotic manipulation from abstract to concrete,” arXiv preprint arXiv:2502.21257 , 2025

2025

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Google, “Gemini robotics brings ai into the physical world,” https://deepmind.google/discover/blog/gemini-robotics-brings-ai-into-the-physical-world/ , 2025

2025

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Nvidia, “Nvidia isaac gr00t n1: An open foundation model for humanoid robots,” https://research.nvidia.com/publication/2025-03_nvidia-isaac-gr00t-n1-open-foundation-model-humanoid-robots , 2025

2025

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H. Wu, Y. Yao, S. Liu, Z. Liu, X. Fu, X. Han, X. Li, H.-L. Zhen, T. Zhong, and M. Yuan, “Unlocking efficient long-to-short llm reasoning with model merging,” arXiv preprint arXiv:2503.20641 , 2025

2025

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I. Ong, A. Almahairi, V. Wu, W.-L. Chiang, T. Wu, J. E. Gonzalez, M. W. Kadous, and I. Stoica, “RouteLLM: Learning to route LLMs from preference data,” in Proc. of ICLR , 2025

2025

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Y.-N. Chuang, H. Zhou, P. K. Sarma, P. Gopalan, J. Boccio, S. Bolouki, and X. Hu, “Learning to route llms with confidence tokens,” arXiv preprint arXiv:2410.13284 , 2025

2025

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Y.-N. Chuang, L. Yu, G. Wang, L. Zhang, Z. Liu, X. Cai, Y. Sui, V. Braverman, and X. Hu, “Confident or seek stronger: Exploring uncertainty-based on-device llm routing from benchmarking to generalization,” arXiv preprint arXiv:2502.04428 , 2025

2025

Closest in time.

A. Patel, S. Bhattamishra, and N. Goyal, “Are NLP models really able to solve simple math word problems?” in Proc. of NAACL , 2021, pp. 2080–2094

2094

Closest in time.