AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912819998359552 |
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| author | Miao, Yongliang Liang, Yangyang Du, Mengnan |
| author_facet | Miao, Yongliang Liang, Yangyang Du, Mengnan |
| contents | Reward modeling is essential for aligning large language models with human preferences, yet predominant architectures rely on a static pooling strategy to condense sequences into scalar scores. This paradigm, however, suffers from two key limitations: a static inductive bias that misaligns with task-dependent preference signals, and a representational mismatch, as the backbone is optimized for generation rather than fine-grained discrimination. To address this, we propose AdaJudge, a unified framework that jointly adapts representation and aggregation. AdaJudge first refines backbone representations into a discrimination-oriented space via gated refinement blocks. It then replaces the static readout with an adaptive multi-view pooling module that dynamically routes and combines evidence. Extensive experiments on RM-Bench and JudgeBench show that AdaJudge outperforms strong off-the-shelf reward models and traditional pooling baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08097 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling Miao, Yongliang Liang, Yangyang Du, Mengnan Computation and Language Machine Learning Reward modeling is essential for aligning large language models with human preferences, yet predominant architectures rely on a static pooling strategy to condense sequences into scalar scores. This paradigm, however, suffers from two key limitations: a static inductive bias that misaligns with task-dependent preference signals, and a representational mismatch, as the backbone is optimized for generation rather than fine-grained discrimination. To address this, we propose AdaJudge, a unified framework that jointly adapts representation and aggregation. AdaJudge first refines backbone representations into a discrimination-oriented space via gated refinement blocks. It then replaces the static readout with an adaptive multi-view pooling module that dynamically routes and combines evidence. Extensive experiments on RM-Bench and JudgeBench show that AdaJudge outperforms strong off-the-shelf reward models and traditional pooling baselines. |
| title | AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.08097 |