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The demand for synthetic data in mathematical reasoning has increased due to its potential to enhance the mathematical capabilities of large language models (LLMs).
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Large language models are zero-shot reasoners
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
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Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman. 2022 · 2022
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WT Gowers, Ben Green, Freddie Manners, and Terence Tao. 2023 · 2023
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Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothée Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, and Yuhuai Wu. 2023b · 2023
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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
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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
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Llama 2: Open foundation and fine-tuned chat models
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
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Reverse question answering: Can an LLM write a question so hard (or bad) that it can’t answer?
Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng, Jordan Boyd-Graber, and Rachel Rudinger. 2024 · 2024
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Ensembling large language models with process reward-guided tree search for better complex reasoning
Sungjin Park, Xiao Liu, Yeyun Gong, and Edward Choi. 2024 · 2024
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Improving autoformalization using type checking
Auguste Poiroux, Gail Weiss, Viktor Kunčak, and Antoine Bosselut. 2024 · 2024
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Verification and refinement of natural language explanations through llm-symbolic theorem proving
Xin Quan, Marco Valentino, Louise A Dennis, and André Freitas. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. 2024 · 2024
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Alphamath almost zero: Process supervision without process
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Formalising half of a graduate textbook on number theory (short paper)
Manuel Eberl, Anthony Bordg, Lawrence C Paulson, and Wenda Li. 2024 · 2024
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CRITIC: large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. 2024a · 2024
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Tora: A tool-integrated reasoning agent for mathematical problem solving
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, et al. 2024 · 2024
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Mathscale: Scaling instruction tuning for mathematical reasoning
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Mathstral-7b-v0.1
The Mistral AI Team. 2024 · 2024
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Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data
Shubham Toshniwal, Wei Du, Ivan Moshkov, Branislav Kisacanin, Alexan Ayrapetyan, and Igor Gitman. 2024 · 2024
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Lego-prover: Neural theorem proving with growing libraries
Haiming Wang, Huajian Xin, Chuanyang Zheng, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, Jian Yin, Zhenguo Li, and Xiaodan Liang. 2024a · 2024
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Mathcoder: Seamless code integration in llms for enhanced mathematical reasoning
Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2024b · 2024
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Deepseek-prover: Advancing theorem proving in llms through large-scale synthetic data
Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, and Xiaodan Liang. 2024 · 2024
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Faithful logical reasoning via symbolic chain-of-thought
Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu. 2024 · 2024
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Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2024 · 2024
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Don’t trust: Verify - grounding LLM quantitative reasoning with autoformalization
Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, and Yuhuai Wu. 2024 · 2024
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rstar-math: Small LLMs can master math reasoning with self-evolved deep thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang. 2025 · 2025
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Deepseek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. 2025 · 2025
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Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025 · 2025
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Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, et al. 2025 · 2025
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Learning to plan & reason for evaluation with thinking-llm-as-a-judge
Swarnadeep Saha, Xian Li, Marjan Ghazvininejad, Jason Weston, and Tianlu Wang. 2025 · 2025
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Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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