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This is the official code for the paper RLTF: Reinforcement Learning from Unit Test Feedback.

The code requires some dependencies as specified in requirements.txt. Please follow the relevant libraries to install or run:

pip install -r requirements.txt

• APPS: Please follow the downloading and preprocessing instructions provided here.
• MBPP: The dataset is available here.

Download and unzip all files into the data folder.

Coming soon.

• CodeT5: sh script/train_actor_deepspeed.sh
• CodeGEN: sh script/train_actor_codegen_deepspeed.sh

• CodeT5: python script/generate_online_parallel.py
• CodeGEN: python script/generate_codegen_online_parallel.py

After running the online generation for a short period and accumulating a certain number of samples：

• CodeT5: sh script/train_actor_rl_online_v1_deepspeed.sh
• CodeGEN: sh script/train_actor_rl_codegen_online_v1_deepspeed.sh

Generate Program:

• CodeT5: python script/generate_parallel.py
• CodeGEN: python script/generate_parallel_codegen.py

Run Unit Test：

• sh script/run_unit_tests.sh

Compute pass@k：

• python compute_pass_at_k_metric.py

If you find the paper or the source code useful to your projects, please cite the following bibtex:

@misc{liu2023rltf,
      title={RLTF: Reinforcement Learning from Unit Test Feedback}, 
      author={Jiate Liu and Yiqin Zhu and Kaiwen Xiao and Qiang Fu and Xiao Han and Wei Yang and Deheng Ye},
      year={2023},
      eprint={2307.04349},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

The code is released under BSD 3-Clause - see LICENSE.txt for details.

This code is developed from other open source projects: including CodeRL, APPS, and transformers. We thank the original contributors of these works for open-sourcing their valuable source codes.