ReCode: Updating Code API Knowledge with Reinforcement Learning

The official code implementation of the paper "ReCode: Updating Code API Knowledge with Reinforcement Learning".

Environment Installation

We recommend using conda to deploy the environment:

conda create -n rec python==3.10
conda activate rec
git clone https://github.com/zjunlp/ReCode.git
cd ReCode

Our project relies both on trl and verl, so it needs to be installed:

cd src/DAPO/
git clone https://github.com/volcengine/verl.git
USE_MEGATRON=0 bash scripts/install_vllm_sglang_mcore.sh
pip3 install --no-deps -e .
pip3 install trl

Data Preparation

We have uploaded our collected data to Hugging Face, you can download it here. Each piece of data contains the following six fields:

• Dependency specifies the library;
• New_version indicates the required version of the library;
• Description provides an explanation of the code's functionality;
• Update_info notes the details of the updates
• Old_code contains the original code snippets
• New_code contains the updated code snippets

The following illustrates an example:

{
    "dependency": "Numpy", 
    "new_version": "==2.2.0", 
    "description": "The code demonstrates how to assign a custom docstring to a NumPy ufunc to enhance its documentation and improve code readability.", 
    "update_info": "_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc should be used instead.", 
    "old_code": "import numpy as np\n\nmy_ufunc = np.frompyfunc(lambda x: x**2, 1, 1)\n\nnp._add_newdoc_ufunc(my_ufunc, \"This is a custom ufunc that squares the input.\")", 
    "new_code": "import numpy as np\n\nmy_ufunc = np.frompyfunc(lambda x: x**2, 1, 1)\n\nmy_ufunc.__doc__ = \"This is a custom ufunc that squares the input.\""
}

Run

GRPO Training

For GRPO training, we provide the script located at scripts/grpo.sh. Before using it, simply fill in the necessary content within the script.

bash scripts/grpo.sh

DAPO Training

Due to the requirements of the verl library for data format, it is necessary to preprocess the data before training.

python3 src/DAPO/data_process.py

After that, you can directly execute the script:

bash scripts/dapo.sh

Acknowledgement

Our code is build on trl and verl. Thanks to their great works.