Papers with fine-grained discrimination
AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling (2026.acl-long)
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| Challenge: | Existing reward models rely on a static pooling strategy to condense sequences into scalar scores, which is ill-suited for fine-grained discrimination. |
| Approach: | They propose a framework that jointly adapts representation and aggregation to address these limitations by integrating a static inductive bias with a representational mismatch. |
| Outcome: | Experiments on RM-Bench and JudgeBench show that AdaJudge outperforms strong off-the-shelf reward models and traditional pooling baselines. |
MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation (2026.findings-acl)
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| Challenge: | Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data. |
| Approach: | They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition. |
| Outcome: | The proposed framework smooths the learning curve in medical reasoning by facilitating domain adaptation before advancing to complex long-chain reasoning. |