# LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG Source: [LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG]() Collection index: [llms.txt]() Data updated: 2026-01-29T05:00:05.136Z Authors: Manish Chandra, Debasis Ganguly, Iadh Ounis Published: 2026-01-27T12:26:31.000Z ## Abstract Most conventional Retrieval-Augmented Generation (RAG) pipelines rely on relevance-based retrieval, which often misaligns with utility -- that is, whether the retrieved passages actually improve the quality of the generated text specific to a downstream task such as question answering or query-based summarization. The limitations of existing utility-driven retrieval approaches for RAG are that, firstly, they are resource-intensive typically requiring query encoding, and that secondly, they do not involve listwise ranking loss during training. The latter limitation is particularly critical, as the relative order between documents directly affects generation in RAG. To address this gap, we propose Lightweight Utility-driven Reranking for Efficient RAG (LURE-RAG), a framework that augments any black-box retriever with an efficient LambdaMART-based reranker. Unlike prior methods, LURE-RAG trains the reranker with a listwise ranking loss guided by LLM utility, thereby directly optimizing the ordering of retrieved documents. Experiments on two standard datasets demonstrate that LURE-RAG achieves competitive performance, reaching 97-98% of the state-of-the-art dense neural baseline, while remaining efficient in both training and inference. Moreover, its dense variant, UR-RAG, significantly outperforms the best existing baseline by up to 3%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-domain Question Answering on TriviaQA (TQA)](): Open-domain Question Answering; TriviaQA (TQA); Accuracy: 0.4177. - [Question Answering on TriviaQA (test) (Acc and F1)](): Question Answering; TriviaQA (test); F1 Score: 42.39. - [Question Answering on NQ (test)](): Question Answering; NQ (test); EM Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()