Concise Chain-of-Thought

Research on Concise Chain-of-Thought

Research papers include:

• Devmallya Karar, Oct 4, 2024, Chain-Of-Thought ( CoT ) in Large Language Models prompting and Concise CoT with Code implementation using Python and PyTorch, https://medium.com/@devmallyakarar/chain-of-thought-cot-in-large-language-models-prompting-and-concise-cot-with-code-82821f9a832d
• Cobus Greyling, Jan 24, 2024, Concise Chain-of-Thought (CCoT) Prompting, Traditional CoT comes at a cost of increased output token usage, CCoT prompting is a prompt-engineering technique which is aimed at reducing LLM response verbosity & inference time. https://cobusgreyling.substack.com/p/concise-chain-of-thought-ccot-prompting
• Matthew Renze, Erhan Guven 19 Oct 2024 (v3), The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models, https://arxiv.org/abs/2401.05618 https://github.com/matthewrenze/jhu-concise-cot (The original paper on Concise CoT.)
• Waleed Kadous, May 17, 2023, Numbers every LLM Developer should know, https://www.anyscale.com/blog/num-every-llm-developer-should-know (Includes discussion of "be concise" prompting.)
• Sania Nayab, Giulio Rossolini, Giorgio Buttazzo, Nicolamaria Manes, Fabrizio Giacomelli, 29 Jul 2024, Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost, https://arxiv.org/abs/2407.19825
• Junjie Liu, Shaotian Yan, Chen Shen, Liang Xie, Wenxiao Wang, Jieping Ye, 13 Jun 2024 (v4), Concise and Organized Perception Facilitates Reasoning in Large Language Models, https://arxiv.org/abs/2310.03309
• Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He, 25 Feb 2025, Chain of Draft: Thinking Faster by Writing Less, https://arxiv.org/abs/2502.18600 (Concise CoT method using a per-step inference budget.)
• Tergel Munkhbat, Namgyu Ho, Seohyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun, 27 Feb 2025, Self-Training Elicits Concise Reasoning in Large Language Models, https://arxiv.org/abs/2502.20122 https://github.com/TergelMunkhbat/concise-reasoning
• Ayeong Lee, Ethan Che, Tianyi Peng, 3 Mar 2025, How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach, https://arxiv.org/abs/2503.01141
• Pranjal Aggarwal, Sean Welleck, 6 Mar 2025, L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning, https://arxiv.org/abs/2503.04697 https://www.cmu-l3.github.io/l1 (Efficient CoT by controlling the length of intermediate step outputs.)
• Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, Yueting Zhuang, 13 Mar 2025 (v2), InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models, https://arxiv.org/abs/2503.06692
• Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, Yu Cheng, 27 Mar 2025, A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond, https://arxiv.org/abs/2503.21614
• Chengyu Huang, Zhengxin Zhang, Claire Cardie, 16 May 2025, HAPO: Training Language Models to Reason Concisely via History-Aware Policy Optimization, https://arxiv.org/abs/2505.11225
• Yuhui Xu, Hanze Dong, Lei Wang, Doyen Sahoo, Junnan Li, Caiming Xiong, 21 May 2025 (v2), Scalable Chain of Thoughts via Elastic Reasoning, https://arxiv.org/abs/2505.05315 https://github.com/SalesforceAIResearch/Elastic-Reasoning
• Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang, 8 May 2025, ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning, https://arxiv.org/abs/2505.04881
• Sohyun An, Ruochen Wang, Tianyi Zhou, Cho-Jui Hsieh, 27 May 2025, Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models, https://arxiv.org/abs/2505.21765
• Ye Yu, Yaoning Yu, Haohan Wang, 12 Jun 2025, PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models, https://arxiv.org/abs/2506.10716
• Sicheng Feng, Gongfan Fang, Xinyin Ma, Xinchao Wang, 15 Apr 2025, Efficient Reasoning Models: A Survey, https://arxiv.org/abs/2504.10903

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