Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction

• URL: http://arxiv.org/abs/2508.18780v1
• Date: Tue, 26 Aug 2025 08:04:04 GMT
• Title: Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
• Authors: Yilin Li, Xunjian Yin, Yilin Chen, Xiaojun Wan,
• Abstract summary: Grammatical error correction is a significant task in NLP.<n>We propose a novel framework based on Rule-Based RL.<n>We show that our framework achieves textbfstate-of-the-artperformance, with a notable increase in textbfrecall.
• Score: 42.61179110228965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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