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We propose CURE, a novel reinforcement learning framework with a dedicated reward design that co-evolves coding and unit test generation capabilities based on their interaction outcomes, without any ground-truth code as supervision.
Using model checking to generate tests from requirements specifications
Angelo Gargantini and Constance Heitmeyer · 1999
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Symstra: A framework for generating object-oriented
Unit Tests Using Symbolic Execution · 2005
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Randoop: feedback-directed random testing for java
Carlos Pacheco and Michael D Ernst · 2007
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Combining unit-level symbolic execution and system-level concrete execution for testing nasa software
Corina S Pǎsǎreanu, Peter C Mehlitz, David H Bushnell, Karen Gundy-Burlet, Michael Lowry, Suzette Person, and Mark Pape · 2008
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Evosuite: automatic test suite generation for object-oriented software
Gordon Fraser and Andrea Arcuri · 2011
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Automated test generation using model checking: an industrial evaluation
Eduard P Enoiu, Adnan Čaušević, Thomas J Ostrand, Elaine J Weyuker, Daniel Sundmark, and Paul Pettersson · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Unit test case generation with transformers and focal context
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, and Neel Sundaresan · 2020
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Ipo: Interior-point policy optimization under constraints
Yongshuai Liu, Jiaxin Ding, and Xin Liu · 2020
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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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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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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 · 2022
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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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Let’s verify step by step
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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Enhancing large language models in coding through multi-perspective self-consistency
Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, and Nan Duan · 2023
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Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou · 2023
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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
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An empirical evaluation of using large language models for automated unit test generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip · 2023
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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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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
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
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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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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Livebench: A challenging, contamination-free llm benchmark
Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Ben Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, et al · 2024
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Livecodebench: Holistic and contamination free evaluation of large language models for code
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica · 2024
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An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
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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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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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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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o1-coder: an o1 replication for coding
Yuxiang Zhang, Shangxi Wu, Yuqi Yang, Jiangming Shu, Jinlin Xiao, Chao Kong, and Jitao Sang · 2024
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Stepcoder: Improve code generation with reinforcement learning from compiler feedback
Shihan Dou, Yan Liu, Haoxiang Jia, Limao Xiong, Enyu Zhou, Wei Shen, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, et al · 2024
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Acecoder: An effective prompting technique specialized in code generation
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin · 2024
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Code generation with alphacodium: From prompt engineering to flow engineering
Tal Ridnik, Dedy Kredo, and Itamar Friedman · 2024
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A3test: Assertion-augmented automated test case generation
Saranya Alagarsamy, Chakkrit Tantithamthavorn, and Aldeida Aleti · 2024
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A large-scale empirical study on fine-tuning large language models for unit testing
Ye Shang, Quanjun Zhang, Chunrong Fang, Siqi Gu, Jianyi Zhou, and Zhenyu Chen · 2024
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Dapo: An open-source llm reinforcement learning system at scale
Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al · 2025
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Reasonflux: Hierarchical llm reasoning via scaling thought templates
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Genprm: Scaling test-time compute of process reward models via generative reasoning
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Inference-time scaling for generalist reward modeling
Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, and Yu Wu · 2025
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Dynamic scaling of unit tests for code reward modeling
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S*: Test time scaling for code generation
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Learning to generate unit tests for automated debugging
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Demystifying long chain-of-thought reasoning in llms
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue · 2025
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Dynamic chain-of-thought: Towards adaptive deep reasoning
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Scoreflow: Mastering llm agent workflows via score-based preference optimization
Yinjie Wang, Ling Yang, Guohao Li, Mengdi Wang, and Bryon Aragam · 2025
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Mmada: Multimodal large diffusion language models
Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang · 2025
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Codeforces
Guilherme Penedo, Anton Lozhkov, Hynek Kydlíček, Loubna Ben Allal, Edward Beeching, Agustín Piqueres Lajarín, Quentin Gallouédec, Nathan Habib, Lewis Tunstall, and Leandro von Werra · 2025
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang · 2025
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Qwq-32b: Embracing the power of reinforcement learning, March 2025
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