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Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks by scaling test-time compute and exhibiting human-like deep thinking.
Montreal neural machine translation systems for wmt’15
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Correcting length bias in neural machine translation
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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
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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
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David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Multimodal structured generation: Cvpr’s 2nd mmfm challenge technical report
Franz Louis Cesista · 2024
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Do not think that much for 2+3=? on the overthinking of o1-like llms, 2024
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Qwq: Reflect deeply on the boundaries of the unknown, November 2024
Qwen · 2024
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Better instruction-following through minimum bayes risk
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Phd knowledge not required: A reasoning challenge for large language models
Carolyn Jane Anderson, Joydeep Biswas, Aleksander Boruch-Gruszecki, Federico Cassano, Molly Q Feldman, Arjun Guha, Francesca Lucchetti, and Zixuan Wu · 2025
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OpenAI · 2024
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Daman Arora and Andrea Zanette · 2025
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When more is less: Understanding chain-of-thought length in llms
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