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The recent DeepSeek-R1 release has demonstrated the immense potential of reinforcement learning (RL) in enhancing the general reasoning capabilities of large language models (LLMs).
Pattern Matching: The Gestalt Approach
John W. Ratcliff and David E. Metzener · 1988
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Program synthesis with large language models, 2021
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton · 2021
Earlier work this paper cites.
Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Earlier work this paper cites.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
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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
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Less training, more repairing please: Revisiting automated program repair via zero-shot learning, 2022
Chunqiu Steven Xia and Lingming Zhang · 2022
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Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen · 2023
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Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models, 2023
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang · 2023
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Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang · 2023
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Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou · 2023
Earlier work this paper cites.
Impact of code language models on automated program repair, 2023
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan · 2023
Earlier work this paper cites.
Swe-bench: Can language models resolve real-world github issues?, 2023
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
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Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen · 2023
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Starcoder: may the source be with you!, 2023
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries · 2023
Earlier work this paper cites.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
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Code llama: Open foundation models for code, 2023
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve · 2023
Earlier work this paper cites.
An empirical evaluation of using large language models for automated unit test generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip · 2023
Earlier work this paper cites.
Copiloting the copilots: Fusing large language models with completion engines for automated program repair, 2023
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang · 2023
Earlier work this paper cites.
Automated program repair in the era of large pre-trained language models
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang · 2023
Cited alongside, same era.
No more manual tests? evaluating and improving chatgpt for unit test generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, and Xin Peng · 2023
Cited alongside, same era.
Repocoder: Repository-level code completion through iterative retrieval and generation, 2023
Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen · 2023
Cited alongside, same era.
Meta large language model compiler: Foundation models of compiler optimization, 2024
Chris Cummins, Volker Seeker, Dejan Grubisic, Baptiste Roziere, Jonas Gehring, Gabriel Synnaeve, and Hugh Leather · 2024
Cited alongside, same era.
Magicoder: Empowering code generation with OSS-instruct
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang · 2024
Later among the works it cites.
Agentless: Demystifying llm-based software engineering agents, 2024
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, and Lingming Zhang · 2024
Later among the works it cites.
Autocoderover: Autonomous program improvement, 2024
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury · 2024
Later among the works it cites.
Commit0: Library generation from scratch, 2024
Wenting Zhao, Nan Jiang, Celine Lee, Justin T Chiu, Claire Cardie, Matthias Gallé, and Alexander M Rush · 2024
Later among the works it cites.
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
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DeepSeek-AI · 2024
Cited alongside, same era.
Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence, 2024
DeepSeek-AI, Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y. Wu, Yukun Li, Huazuo Gao, Shirong Ma, Wangding Zeng, Xiao Bi, Zihui Gu, Hanwei Xu, Damai Dai, Kai Dong, Liyue Zhang, Yishi Piao, Zhibin Gou, Zhenda Xie, Zhewen Hao, Bingxuan Wang, Junxiao Song, Deli Chen, Xin Xie, Kang Guan, Yuxiang You, Aixin Liu, Qiushi Du, Wenjun Gao, Xuan Lu, Qinyu Chen, Yaohui Wang, Chengqi Deng, Jiashi Li, Chenggang Zhao, Chong Ruan, Fuli Luo, and Wenfeng Liang · 2024
Cited alongside, same era.
Fewer truncations improve language modeling
Hantian Ding, Zijian Wang, Giovanni Paolini, Varun Kumar, Anoop Deoras, Dan Roth, and Stefano Soatto · 2024
Cited alongside, same era.
The llama 3 herd of models, 2024
AI@Meta: Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, and Angela Fan et al · 2024
Cited alongside, same era.
Aider is ai pair programming in your terminal
Paul Gauthier · 2024
Cited alongside, same era.
CRUXEval: A benchmark for code reasoning, understanding and execution
Alex Gu, Baptiste Roziere, Hugh James Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida Wang · 2024
Cited alongside, same era.
Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang · 2024
Cited alongside, same era.
MiniCPM: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Ganqu Cui, Chaoqun He, Weilin Zhao, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Xinrong Zhang, Zhen Leng Thai, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, dahai li, Zhiyuan Liu, and Maosong Sun · 2024
Cited alongside, same era.
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al · 2024
Later among the works it cites.
Albert Örwall · 2024
Later among the works it cites.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
DeepSeek-AI · 2025
Closest in time.
Rlef: Grounding code llms in execution feedback with reinforcement learning, 2025
Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Quentin Carbonneaux, Taco Cohen, and Gabriel Synnaeve · 2025
Closest in time.
Github rest api documentation
GitHub · 2025
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Gh archive
Ilya Grigorik · 2025
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Swe-bench leaderboard
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2025
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simple-evals
OpenAI · 2025
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Introducing swe-bench verified
OpenAI · 2025
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Approximating kl divergence
John Schulman · 2025
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Swe-fixer: Training open-source llms for effective and efficient github issue resolution, 2025
Chengxing Xie, Bowen Li, Chang Gao, He Du, Wai Lam, Difan Zou, and Kai Chen · 2025
Closest in time.
Demystifying long chain-of-thought reasoning in llms, 2025
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue · 2025
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Acecoder: Acing coder rl via automated test-case synthesis
Huaye Zeng, Dongfu Jiang, Haozhe Wang, Ping Nie, Xiaotong Chen, and Wenhu Chen · 2025
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