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        <title>Edit-Aware Reward Modeling for Chinese Grammatical Error Correction</title>
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        <namePart type="given">Yilin</namePart>
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            <namePart type="given">Maria</namePart>
            <namePart type="family">Liakata</namePart>
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    <abstract>While large language models have achieved remarkable success in various natural language processing tasks, their potential in grammatical error correction remains underexplored. Recent work has applied reinforcement learning with rule-based rewards to CGEC, but these approaches rely on coarse-grained binary signals (exact match or not) that fail to capture fine-grained quality distinctions among correction candidates. In this paper, we propose Edit-Aware Reward Model (EARM), a novel reward modeling framework that explicitly incorporates edit-awareness into preference learning for CGEC. EARM introduces a dual-granularity training objective that jointly optimizes sentence-level and token-level weighted Bradley-Terry ranking losses, where edit tokens receive higher importance weights. When integrated with GRPO, our approach achieves 61.29/63.08 on FCGEC/NaCGEC (single output), and 65.04/64.59 with best-of-16 reranking, surpassing previous best by 5.41 and 1.80 points. Extensive experiments demonstrate that learned edit-aware rewards significantly outperform rule-based alternatives for CGEC preference optimization.</abstract>
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%0 Conference Proceedings
%T Edit-Aware Reward Modeling for Chinese Grammatical Error Correction
%A Li, Yilin
%A Wan, Xiaojun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-wan-2026-edit
%X While large language models have achieved remarkable success in various natural language processing tasks, their potential in grammatical error correction remains underexplored. Recent work has applied reinforcement learning with rule-based rewards to CGEC, but these approaches rely on coarse-grained binary signals (exact match or not) that fail to capture fine-grained quality distinctions among correction candidates. In this paper, we propose Edit-Aware Reward Model (EARM), a novel reward modeling framework that explicitly incorporates edit-awareness into preference learning for CGEC. EARM introduces a dual-granularity training objective that jointly optimizes sentence-level and token-level weighted Bradley-Terry ranking losses, where edit tokens receive higher importance weights. When integrated with GRPO, our approach achieves 61.29/63.08 on FCGEC/NaCGEC (single output), and 65.04/64.59 with best-of-16 reranking, surpassing previous best by 5.41 and 1.80 points. Extensive experiments demonstrate that learned edit-aware rewards significantly outperform rule-based alternatives for CGEC preference optimization.
%U https://aclanthology.org/2026.acl-long.1900/
%P 40945-40957

Markdown (Informal)

[Edit-Aware Reward Modeling for Chinese Grammatical Error Correction](https://aclanthology.org/2026.acl-long.1900/) (Li & Wan, ACL 2026)

• Edit-Aware Reward Modeling for Chinese Grammatical Error Correction (Li & Wan, ACL 2026)

ACL

• Yilin Li and Xiaojun Wan. 2026. Edit-Aware Reward Modeling for Chinese Grammatical Error Correction. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40945–40957, San Diego, California, United States. Association for Computational Linguistics.