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We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T. P., Simonyan, K., and Hassabis, D · 2017
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Solving rubik’s cube with a robot hand
Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., Ribas, R., et al · 2019
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pondé, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D. W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Babuschkin, I., Balaji, S., Jain, S., Carr, A., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M. M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Earlier work this paper cites.
Measuring coding challenge competence with APPS
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Earlier work this paper cites.
Fireact: Toward language agent fine-tuning
Chen, B., Shu, C., Shareghi, E., Collier, N., Narasimhan, K., and Yao, S · 2023
Earlier work this paper cites.
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
Earlier work this paper cites.
React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., and Cao, Y · 2023
Earlier work this paper cites.
Agenttuning: Enabling generalized agent abilities for llms
Zeng, A., Liu, M., Lu, R., Wang, B., Liu, X., Dong, Y., and Tang, J · 2023
Earlier work this paper cites.
Scaling data collection for training software engineering agents
Badertdinov, I., Trofimova, M., Anapolskiy, Y., Abramov, S., Zainullina, K., Golubev, A., Polezhaev, S., Litvintseva, D., Karasik, S., Fisin, F., Skvortsov, S., Nekrashevich, M., Shevtsov, A., and Yangel, B · 2024
Earlier work this paper cites.
Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning
Bai, H., Zhou, Y., Cemri, M., Pan, J., Suhr, A., Levine, S., and Kumar, A · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., R’e, C., and Mirhoseini, A · 2024
Cited alongside, same era.
Coder: Issue resolving with multi-agent and task graphs
Chen, D., Lin, S., Zeng, M., Zan, D., Wang, J.-G., Cheshkov, A., Sun, J., Yu, H., Dong, G., Aliev, A., Wang, J., Cheng, X., Liang, G., Ma, Y., Bian, P., Xie, T., and Wang, Q · 2024
Cited alongside, same era.
Leveraging training and search for better software engineering agents
Golubev, A., Polezhaev, S., Zainullina, K., Trofimova, M., Badertdinov, I., Anapolskiy, Y., Litvintseva, D., Karasik, S., Fisin, F., Skvortsov, S., Nekrashevich, M., Shevtsov, A., Abramov, S., and Yangel, B · 2024
Cited alongside, same era.
Enhancing program synthesis with large language models using many-objective grammar-guided genetic programming
Tao, N., Ventresque, A., Nallur, V., and Saber, T · 2024
Closest in time.
Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving
Tong, Y., Zhang, X., Wang, R., Wu, R. M., and He, J · 2024
Closest in time.
Easily finetune and train LLMs. Get faster with unsloth
Unsloth Team · 2024
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Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations
Wang, P., Li, L., Shao, Z., Xu, R., Dai, D., Li, Y., Chen, D., Wu, Y., and Sui, Z · 2024
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Executable code actions elicit better LLM agents
Wang, X., Chen, Y., Yuan, L., Zhang, Y., Li, Y., Peng, H., and Ji, H · 2024
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Divide-or-conquer? which part should you distill your llm?
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R2E: turning any github repository into a programming agent environment
Jain, N., Shetty, M., Zhang, T., Han, K., Sen, K., and Stoica, I · 2024
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K. R · 2024
Cited alongside, same era.
Ma, Y., Cao, R., Cao, Y., Zhang, Y., Chen, J., Liu, Y., Liu, Y., Li, B., Huang, F., and Li, Y · 2024
Cited alongside, same era.
Modal: High-performance AI infrastructure
Modal · 2024
Cited alongside, same era.
Evaluation of LLMs as Coding Agents on SWE-Bench (at 30x Speed!)
Neubig, G. and Wang, X · 2024
Cited alongside, same era.
Autonomous evaluation and refinement of digital agents
Pan, J., Zhang, Y., Tomlin, N., Zhou, Y., Levine, S., and Suhr, A · 2024
Cited alongside, same era.
torchtune: PyTorch native post-training library
PyTorch Team · 2024
Cited alongside, same era.
Qwen2.5: A party of foundation models, September 2024
Qwen Team · 2024
Cited alongside, same era.
Wu, Z., Bai, H., Zhang, A., Gu, J., Vinod Vydiswaran, V., Jaitly, N., and Zhang, Y · 2024
Closest in time.
Agentgym: Evolving large language model-based agents across diverse environments
Xi, Z., Ding, Y., Chen, W., Hong, B., Guo, H., Wang, J., Yang, D., Liao, C., Guo, X., He, W., Gao, S., Chen, L., Zheng, R., Zou, Y., Gui, T., Zhang, Q., Qiu, X., Huang, X., Wu, Z., and Jiang, Y.-G · 2024
Closest in time.
Agentless: Demystifying llm-based software engineering agents
Xia, C. S., Deng, Y., Dunn, S., and Zhang, L · 2024
Closest in time.
Swe-agent: Agent-computer interfaces enable automated software engineering
Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., and Press, O · 2024
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Advancing LLM reasoning generalists with preference trees
Yuan, L., Cui, G., Wang, H., Ding, N., Wang, X., Deng, J., Shan, B., Chen, H., Xie, R., Lin, Y., Liu, Z., Zhou, B., Peng, H., Liu, Z., and Sun, M · 2024
Closest in time.
Fine-tuning large vision-language models as decision-making agents via reinforcement learning
Zhai, Y., Bai, H., Lin, Z., Pan, J., Tong, S., Zhou, Y., Suhr, A., Xie, S., LeCun, Y., Ma, Y., and Levine, S · 2024
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Commit0: Library generation from scratch, 2024
Zhao, W., Jiang, N., Lee, C., Chiu, J. T., Cardie, C., Gallé, M., and Rush, A. M · 2024
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Moatless Tool
Örwall, A · 2024
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