Decoder-only rerankers are central to Retrieval-Augmented Generation (RAG). However, generalist models miss domain-specific nuances in high-stakes fields like finance and law, and naive fine-tuning causes surface-form overfitting and catastrophic forgetting. To address this challenge, we introduce R2R, a domain-aware framework that combines dynamic expert routing with a two-stage training strategy, Entity Abstraction for Generalization (EAG). EAG introduces a counter-shortcut mechanism by masking the most predictive surface cues, forcing the reranker to learn domain-invariant relevance patterns rather than memorizing dataset-specific entities. To efficiently activate domain experts, R2R employs a lightweight Latent Semantic Router that probes internal representations from the frozen backbone decoder to select the optimal LoRA expert per query. Extensive experiments across different reranker backbones and diverse domains (legal, medical, and financial) demonstrate that R2R consistently surpasses generalist and single-domain fine-tuned baselines. Our results confirm that R2R is a model-agnostic and modular approach to domain specialization with strong cross-domain robustness.

翻译：仅解码器重排序器在检索增强生成（RAG）中扮演核心角色。然而，通用模型在高风险领域（如金融和法律）中往往忽略领域特有的细微差别，而简单的微调则会导致表面形式过拟合和灾难性遗忘。为解决这一挑战，我们提出了R2R，这是一个具备领域感知能力的框架，结合了动态专家路由与两阶段训练策略——实体抽象泛化（EAG）。EAG通过掩蔽最具预测性的表面线索，引入了一种反捷径机制，迫使重排序器学习领域不变的相关性模式，而非记忆数据集特定的实体。为高效激活领域专家，R2R采用了一个轻量级的潜在语义路由器，该路由器探测来自冻结主干解码器的内部表示，以针对每个查询选择最优的LoRA专家。在不同重排序器主干及多样化领域（法律、医疗和金融）上的大量实验表明，R2R始终优于通用模型和单领域微调的基线方法。我们的结果证实，R2R是一种模型无关且模块化的领域专业化方法，具有强大的跨领域鲁棒性。