超越分数预测：基于强化学习和评分标准奖励的基于大语言模型的作文评分与反馈生成
Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards
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中文总结 AI 辅助
研究基于大语言模型的作文评分与反馈生成，提出RLAES框架，通过强化学习联合优化。引入RFE评估框架，提出AGFO和ACR方法。实验表明RFE能捕捉一致性，RLAES-AGFO在评分性能上最佳，保持反馈质量同时避免反馈退化。
中文摘要 AI 辅助
大语言模型已广泛应用于自动作文评分和自动反馈生成。现有研究主要依赖提示工程或监督微调，对强化学习训练后和反馈质量自动评估的系统研究有限。我们提出RLAES，一个通过强化学习联合优化作文评分和反馈生成的统一大语言模型框架。为使反馈质量可测量、可解释并用于训练，引入基于评分标准的反馈评估（RFE），包括166个细粒度二元评分标准项和以大语言模型为评判的基于作文的反馈评估框架。在此基础上提出自适应门控反馈优化（AGFO），在强化学习中按需激活基于评分标准的反馈奖励，减少评估开销并提高反馈质量。还提出相邻对比推理（ACR）以改善顺序评分校准。实验结果表明RFE框架捕捉作文-反馈一致性，具有强成对判别力且与专家偏好紧密一致。在ASAP基准测试中，RLAES-AGFO在基于大语言模型的方法中取得最佳评分性能（QWK = 0.803），同时保持与GPT-5.5相当的反馈质量并避免仅分数强化学习下的反馈退化。代码和数据集可公开获取。
英文摘要
Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited. We propose RLAES, a unified LLM framework that jointly optimizes essay scoring and feedback generation through RL. To make feedback quality measurable, interpretable, and usable for training, we introduce Rubric-based Feedback Evaluation (RFE), an essay-grounded feedback evaluation framework comprising 166 fine-grained binary rubric items and an LLM-as-judge. Building on RFE, we propose Adaptive Gated Feedback Optimization (AGFO), which activates rubric-based feedback rewards on demand during RL, reducing evaluation overhead while improving feedback quality. We also propose Adjacent Contrastive Reasoning (ACR) to improve ordinal score calibration by explicitly contrasting adjacent score levels. Experimental results show that the RFE framework captures essay-feedback consistency, exhibits strong pairwise discriminative power, and closely aligns with expert preferences. On the ASAP benchmark, RLAES-AGFO achieves the best scoring performance among LLM-based methods (QWK = 0.803), while maintaining feedback quality comparable to GPT-5.5 and avoiding the feedback degradation observed under score-only RL. Code and datasets are publicly available at https://github.com/hellomuyi/RLAES.
