GitHub - XiaoYee/Awesome_Efficient_LRM_Reasoning: 😎 A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, Agent, and Beyond (original) (raw)

@article{qu2025survey,
  title={A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond},
  author={Qu, Xiaoye and Li, Yafu and Su, Zhaochen and Sun, Weigao and Yan, Jianhao and Liu, Dongrui and Cui, Ganqu and Liu, Daizong and Liang, Shuxian and He, Junxian and others},
  journal={arXiv preprint arXiv:2503.21614},
  year={2025}
}

In the age of LRMs, we propose that "Efficiency is the essence of intelligence." Just as a wise human knows when to stop thinking and start deciding, a wise model should know when to halt unnecessary deliberation. An intelligent model should manipulate the token economy, i.e., allocating tokens purposefully, skipping redundancy, and optimizing the path to a solution. Rather than naively traversing every possible reasoning path, it should emulate a master strategist, balancing cost and performance with elegant precision.

To summarize, this survey makes the following key contributions to the literature:

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