Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definitions), over-analysis of simple problems, and superficial exploration of multiple reasoning paths for harder tasks. This inefficiency introduces significant challenges for training, inference, and real-world deployment (e.g., in agent-based systems), where token economy is critical. In this survey, we provide a comprehensive overview of recent efforts aimed at improving reasoning efficiency in LRMs, with a particular focus on the unique challenges that arise in this new paradigm. We identify common patterns of inefficiency, examine methods proposed across the LRM lifecycle, i.e., from pretraining to inference, and discuss promising future directions for research. To support ongoing development, we also maintain a real-time GitHub repository tracking recent progress in the field. We hope this survey serves as a foundation for further exploration and inspires innovation in this rapidly evolving area.
View on arXiv@article{qu2025_2503.21614,
title={ A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond },
author={ Xiaoye Qu and Yafu Li and Zhaochen Su and Weigao Sun and Jianhao Yan and Dongrui Liu and Ganqu Cui and Daizong Liu and Shuxian Liang and Junxian He and Peng Li and Wei Wei and Jing Shao and Chaochao Lu and Yue Zhang and Xian-Sheng Hua and Bowen Zhou and Yu Cheng },
journal={arXiv preprint arXiv:2503.21614},
year={ 2025 }
}