Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this field is still underexplored. Current research predominantly relies on supervised fine-tuning to train LLMs to directly generate the corrected sentence, which limits the model's powerful reasoning ability. To address this limitation, we propose a novel framework based on Rule-Based RL. Through experiments on the Chinese datasets, our Rule-Based RL framework achieves \textbf{state-of-the-art }performance, with a notable increase in \textbf{recall}. This result clearly highlights the advantages of using RL to steer LLMs, offering a more controllable and reliable paradigm for future development in GEC.
@article{arxiv.2508.18780,
title = {Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction},
author = {Yilin Li and Xunjian Yin and Yilin Chen and Xiaojun Wan},
journal= {arXiv preprint arXiv:2508.18780},
year = {2025}
}