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However, standard Reinforcement Learning (RL) methods typically rely on binary rewards (e.g., 0-1 accuracy), thereby ignoring the ordinal structure inherent in regression tasks; for instance, they fail to recognize that predicting 4 is significantly better than predicting 1 when the ground truth is 5. Conversely, existing regression-aware approaches are often confined to Supervised Fine-Tuning (SFT), limiting their ability to explore optimal reasoning paths. To bridge this gap, we propose \\textbf{REAL} (\\underline{RE}gression-\\underline{A}ware Reinforcement \\underline{L}earning), a principled RL framework designed to optimize regression rewards, and also proven to be optimal for correlation metrics. A key technical challenge is that the regression objective is explicitly","title":"REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge","url":"https://arxiv.org/abs/2603.17145","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.17145v2 Announce Type: replace-cross \nAbstract: Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Reinforcement Learning (RL) methods typically rely on binary rewards (e.g., 0-1 accuracy), thereby ignoring the ordinal structure inherent in regression tasks; for instance, they fail to recognize that predicting 4 is significantly better than predicting 1 when the ground truth is 5. Conversely, existing regression-aware approaches are often confined to Supervised Fine-Tuning (SFT), limiting their ability to explore optimal reasoning paths. To bridge this gap, we propose \\textbf{REAL} (\\underline{RE}gression-\\underline{A}ware Reinforcement \\underline{L}earning), a principled RL framework designed to optimize regression rewards, and also proven to be optimal for correlation metrics. 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