ReCode: Updating Code API Knowledge with Reinforcement Learning
Authors: name: Haoze Wu, name: Yunzhi Yao, name: Wenhao Yu, name: Huajun Chen, name: Ningyu Zhang

Large Language Models (LLMs) exhibit remarkable code generation capabilities
but falter when adapting to frequent updates in external library APIs. This
critical limitation, stemming from reliance on outdated API knowledge from
their training data, even with access to current documentation, impedes
reliable code generation in dynamic environments. To tackle this issue, we
propose ReCode (rule-based Reinforcement learning for Code Update), a novel
framework that mimics human programmer adaptation to API changes. Specifically,
we construct a dataset of approximately 2,000 data entries to train the LLMs to
perform version migration based on updated information. Then, we introduce a
modified string similarity metric for code evaluation as the reward for
reinforcement learning. Our experiments demonstrate that ReCode substantially
boosts LLMs' code generation performance in dynamic API scenarios, especially
on the unseen CodeUpdateArena task. Crucially, compared to supervised
fine-tuning, ReCode has less impact on LLMs' general code generation abilities.
We apply ReCode on various LLMs and reinforcement learning algorithms (GRPO and
DAPO), all achieving consistent improvements. Notably, after training,
Qwen2.5-Coder-7B outperforms that of the 32B parameter code instruction-tuned
model and the reasoning model with the same architecture. Code is available at
https://github.com/zjunlp/ReCode.