Rubric-based Feedback Evaluation

RLAES: Reinforcement learning framework for joint essay scoring and feedback generation with rubric rewards

Researchers propose RLAES, a unified LLM framework that jointly optimizes automated essay scoring (AES) and feedback generation via reinforcement learning. The system introduces Rubric-based Feedback Evaluation (RFE) with 166 fine-grained binary rubric items and an LLM-as-judge, plus Adaptive Gated Feedback Optimization (AGFO) and Adjacent Contrastive Reasoning (ACR) for score calibration. On the ASAP benchmark, RLAES-AGFO achieves QWK=0.803 among LLM-based methods while maintaining feedback quality comparable to GPT-5.5. Code and datasets are publicly released.