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Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 1909
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Palm: Pre-training an autoencoding&autoregressive language model for context-conditioned generation
Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, and Luo Si. 2020 · 2004
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Language models are unsupervised multitask learners
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019 · 2019
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Program synthesis with large language models
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Addressing semantic drift in generative question answering with auxiliary extraction
Chenliang Li, Bin Bi, Ming Yan, Wei Wang, and Songfang Huang. 2021 · 2021
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Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022 · 2022
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Seqzero: Few-shot compositional semantic parsing with sequential prompts and zero-shot models
Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, and Diyi Yang. 2022 · 2022
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. 2022 · 2022
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What’s the magic word? a control theory of llm prompting
Aman Bhargava, Cameron Witkowski, Shi-Zhuo Looi, and Matt Thomson. 2023 · 2023
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Alphazero-like tree-search can guide large language model decoding and training
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Towards revealing the mystery behind chain of thought: a theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang. 2024 · 2024
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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, and Kai Chen. 2024 · 2024
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Concise thoughts: Impact of output length on llm reasoning and cost
Sania Nayab, Giulio Rossolini, Giorgio Buttazzo, Nicolamaria Manes, and Fabrizio Giacomelli. 2024 · 2024
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Gpt-4o mini: advancing cost-efficient intelligence
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Xidong Feng, Ziyu Wan, Muning Wen, Ying Wen, Weinan Zhang, and Jun Wang. 2023 · 2023
Cited alongside, same era.
Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. 2023 · 2023
Cited alongside, same era.
Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Cited alongside, same era.
Making language models better reasoners with step-aware verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. 2023 · 2023
Cited alongside, same era.
Language models are hidden reasoners: Unlocking latent reasoning capabilities via self-rewarding
Haolin Chen, Yihao Feng, Zuxin Liu, Weiran Yao, Akshara Prabhakar, Shelby Heinecke, Ricky Ho, Phil Mui, Silvio Savarese, Caiming Xiong, et al. 2024 · 2024
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Hello gpt-4o
OpenAI. 2024b · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Visual agents as fast and slow thinkers
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Alan Wake, Albert Wang, Bei Chen, CX Lv, Chao Li, Chengen Huang, Chenglin Cai, Chujie Zheng, Daniel Cooper, Ethan Dai, et al. 2024 · 2024
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Monte carlo tree search boosts reasoning via iterative preference learning
Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P Lillicrap, Kenji Kawaguchi, and Michael Shieh. 2024 · 2024
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Response length perception and sequence scheduling: An llm-empowered llm inference pipeline
Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo, Xin Jiang, and Yang You. 2024 · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Openai o3-mini: Pushing the frontier of cost-effective reasoning
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Dump: Automated distribution-level curriculum learning for rl-based llm post-training
Zhenting Wang, Guofeng Cui, Kun Wan, and Wentian Zhao. 2025 · 2025
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