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The goal of program synthesis, or code generation, is to generate executable code based on given descriptions.
Introduction to reinforcement learning , volume 135
Richard S Sutton, Andrew G Barto, et al · 1998
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Language models are few-shot learners
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
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Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 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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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Evaluating large language models trained on code.(2021)
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, J Kaplan, H Edwards, Y Burda, N Joseph, G Brockman, et al · 2021
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi · 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, et al · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Self-critiquing models for assisting human evaluators
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Repairing bugs in python assignments using large language models
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Codexglue: A machine learning benchmark dataset for code understanding and generation
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Online and offline reinforcement learning by planning with a learned model
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Palm: Scaling language modeling with pathways
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Pangu-coder: Program synthesis with function-level language modeling
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Incoder: A generative model for code infilling and synthesis
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Jialu Zhang, José Cambronero, Sumit Gulwani, Vu Le, Ruzica Piskac, Gustavo Soares, and Gust Verbruggen · 2022
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Santacoder: don’t reach for the stars!
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A survey on offline reinforcement learning: Taxonomy, review, and open problems
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
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