CONF Coman_EMNLP_2025/IDIAP RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation Coman, Andrei Catalin Sorodoc, Ionuț-Teodor Ribeiro, Leonardo F. R. Byrne, Bill Henderson, James de Gispert, Adrià EXTERNAL http://publications.idiap.ch/attachments/papers/2025/Coman_EMNLP_2025.pdf PUBLIC Empirical Methods in Natural Language Processing 2025 https://www.arxiv.org/abs/2509.26011 URL Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfulness to retrieved context, relevance to the user query, appropriate refusals when context is insufficient, completeness and conciseness of information. To address the lack of publicly available RAG-centric preference datasets and specialised RMs, we introduce RAGferee, a methodology that repurposes question-answering (QA) datasets into preference pairs that prioritise groundedness over stylistic features, enabling the training of contextual RMs better suited to judging RAG responses. Using RAGferee, we curate a small preference dataset of 4K samples and fine-tune RMs ranging from 7B to 24B parameters. Our RAG-centric RMs achieve state-of-the-art performance on ContextualJudgeBench, surpassing existing 70B+ RMs trained on much larger (up to 2.4M samples) general corpora, with an absolute improvement of +15.5%.