CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

Researchers propose CSRP, a three-stage framework combining continual pre-training, chain-of-thought reasoning, and reinforcement learning to improve Chinese grammatical error correction in LLMs. The system achieves state-of-the-art performance on the NACGEC benchmark while addressing the over-correction problem common in supervised fine-tuning approaches.

CSRP represents a meaningful advancement in specialized language model optimization, demonstrating how reinforcement learning can correct fundamental misalignments in error correction tasks. Traditional supervised fine-tuning optimizes for likelihood rather than precision, causing models to over-correct text by making unnecessary edits. This work addresses that gap through an efficiency-aware reward mechanism that explicitly penalizes superfluous changes, a conceptually simple but practically important distinction.

The three-stage approach reflects growing sophistication in LLM training methodology. Continual pre-training on 5.9M balanced samples builds domain-specific linguistic knowledge before fine-tuning, while chain-of-thought reasoning provides interpretability by forcing models to explain their correction logic. The subsequent group relative policy optimization stage leverages RL to align model behavior with evaluation metrics that matter for real-world deployment.

The performance gains are substantial: 50.99 F₀.₅ and 57.17 precision on NACGEC benchmarks, with 59.61 F1 on spelling correction—surpassing GPT-4 by 5.20 points. The 8% relative improvement from RL alignment over SFT baselines validates that metric-aligned optimization meaningfully outperforms likelihood-based training. This finding applies beyond Chinese text to any error correction task where precision and edit efficiency matter.

For the broader AI industry, CSRP exemplifies how specialized frameworks can outperform general-purpose models on focused linguistic tasks. The open-source release enables reproducibility and derivative work. The methodology's emphasis on efficiency rather than maximum changes signals a market trend toward practical, deployable AI systems that avoid costly over-correction errors in production environments.

• →CSRP achieves state-of-the-art Chinese grammatical error correction through a three-stage framework combining pre-training, chain-of-thought reasoning, and efficiency-aware reinforcement learning.
• →Efficiency-aware rewards that penalize unnecessary edits reduce over-correction bias inherent in traditional maximum likelihood estimation approaches.
• →The RL alignment stage contributes 8% relative performance gain over supervised fine-tuning baselines while remaining orthogonal to benefits from large-scale continual pre-training.
• →The method surpasses GPT-4 on spelling correction tasks by 5.20 F1 points, demonstrating specialized frameworks can outperform general-purpose models on focused linguistic problems.
• →Open-source code release enables reproducibility and potential application of efficiency-aware RL optimization to other error correction and text generation tasks.