ACECODER: Acing Coder RL via Automated Test-Case Synthesis

Rank analysis of incomplete block designs: I. the method of paired comparisons

Ralph Allan Bradley and Milton E. Terry. 1952 · 1952

Earlier work this paper cites.

Scaling laws for neural language models

Original

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001

Earlier work this paper cites.

High-dimensional continuous control using generalized advantage estimation

Original

John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and P. Abbeel. 2015 · 2015

Earlier work this paper cites.

Proximal policy optimization algorithms

Original

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017

Earlier work this paper cites.

Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task

Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018

Earlier work this paper cites.

Program synthesis with large language models

Original

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021

Earlier work this paper cites.

Evaluating large language models trained on code

Original

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 · 2021

Earlier work this paper cites.

Measuring coding challenge competence with apps

Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. 2021 · 2021

Earlier work this paper cites.

Codet: Code generation with generated tests

Original

Bei Chen, Fengji Zhang, A. Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022

Earlier work this paper cites.

Coderl: Mastering code generation through pretrained models and deep reinforcement learning

Original

Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven C. H. Hoi. 2022 · 2022

Earlier work this paper cites.

Mistral-7b-instruct-v0.3

Mistral AI. 2023 · 2023

Earlier work this paper cites.

Enhancing large language models in coding through multi-perspective self-consistency

Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, and Nan Duan. 2023 · 2023

Earlier work this paper cites.

Taco: Topics in algorithmic code generation dataset

Original

Rongao Li, Jie Fu, Bo-Wen Zhang, Tao Huang, Zhihong Sun, Chen Lyu, Guang Liu, Zhi Jin, and Ge Li. 2023 · 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 · 2023

Cited alongside, same era.

Wizardcoder: Empowering code large language models with evol-instruct

Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023

Cited alongside, same era.

Code llama: Open foundation models for code

Original

Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, I. Evtimov, Joanna Bitton, Manish P Bhatt, Cristian Cantón Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre D’efossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023

Cited alongside, same era.

Learning performance-improving code edits

Original

Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh. 2023 · 2023

Cited alongside, same era.

Gpt-4o system card

Original

Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. 2024 · 2024

Later among the works it cites.

Livecodebench: Holistic and contamination free evaluation of large language models for code

Original

Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024 · 2024

Later among the works it cites.

Preference optimization for reasoning with pseudo feedback

Original

Fangkai Jiao, Geyang Guo, Xingxing Zhang, Nancy F. Chen, Shafiq Joty, and Furu Wei. 2024 · 2024

Later among the works it cites.

Swe-bench: Can language models resolve real-world github issues?

Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. 2024 · 2024

Later among the works it cites.