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Practical guidance on training Large Language Models (LLMs) to leverage Code Interpreter across diverse tasks remains lacking.
Notes on kullback-leibler divergence and likelihood, 2014
Jonathon Shlens · 2000
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Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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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
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2022
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Language models of code are few-shot commonsense learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig · 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
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 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
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Generating sequences by learning to self-correct
Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi · 2022
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Re3: Generating longer stories with recursive reprompting and revision
Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Chain of code: Reasoning with a language model-augmented code emulator
Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, and Brian Ichter · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Cited alongside, same era.
Codeplan: Repository-level coding using llms and planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, and Shashank Shet · 2024
Cited alongside, same era.
Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?
Yongchao Chen, Jacob Arkin, Yang Zhang, Nicholas Roy, and Chuchu Fan · 2024
Can llms reason in the wild with programs?
Yuan Yang, Siheng Xiong, Ali Payani, Ehsan Shareghi, and Faramarz Fekri · 2024
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
Later among the works it cites.
Codesteer: Symbolic-augmented language models via code/text guidance, 2025
Yongchao Chen, Yilun Hao, Yueying Liu, Yang Zhang, and Chuchu Fan · 2025
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Retool: Reinforcement learning for strategic tool use in llms
Jiazhan Feng, Shijue Huang, Xingwei Qu, Ge Zhang, Yujia Qin, Baoquan Zhong, Chengquan Jiang, Jinxin Chi, and Wanjun Zhong · 2025
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rstar-math: Small llms can master math reasoning with self-evolved deep thinking
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Large language models can plan your travels rigorously with formal verification tools
Yilun Hao, Yongchao Chen, Yang Zhang, and Chuchu Fan · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 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
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Meta-prompting: Enhancing language models with task-agnostic scaffolding
Mirac Suzgun and Adam Tauman Kalai · 2024
Cited alongside, same era.
Mixture-of-agents enhances large language model capabilities
Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou · 2024
Cited alongside, same era.
Learning to reason via program generation, emulation, and search
Nathaniel Weir, Muhammad Khalifa, Linlu Qiu, Orion Weller, and Peter Clark · 2024
Cited alongside, same era.
Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang · 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
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Reinforce++: A simple and efficient approach for aligning large language models
Jian Hu · 2025
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Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han · 2025
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Toolrl: Reward is all tool learning needs
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Qwen2.5 technical report, 2025
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Vl-rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning
Haozhe Wang, Chao Qu, Zuming Huang, Wei Chu, Fangzhen Lin, and Wenhu Chen · 2025
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Swe-rl: Advancing llm reasoning via reinforcement learning on open software evolution
Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I Wang · 2025
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