Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces a fundamental tradeoff between recall and query drift. Generating many reformulated queries can substantially increase recall, yet naively merging or exhaustively reranking their results is prohibitively expensive. In this work, we argue that the core challenge is not reformulation generation itself, but the adaptive selection of reformulations and their retrieved documents under a strict inference budget. We propose ReformIR, a budget-aware retrieval framework that treats query reformulations as first-class features and performs online relevance estimation using a strong reranker as a teacher. Given multiple reformulated queries, ReformIR constructs a large candidate pool and learns a lightweight surrogate model that estimates document utility from reformulation-specific retrieval signals. Under a fixed reranking budget, the surrogate adaptively prioritizes both reformulations and documents, selectively querying a teacher reranker anchored to the original query. This process increases recall while actively suppressing drift through online feature selection over reformulations. We conduct extensive experiments on the MSMARCO passage corpora and TREC Deep Learning benchmarks (DL19-DL22). Our results show that ReformIR consistently outperforms existing reformulation strategies, particularly as the number of reformulations increases, where prior methods suffer from severe quality degradation due to drift. Our findings also suggest a shift in retrieval system design, rather than using large language models as rerankers, their capacity is more effectively leveraged in the reformulation stage with feedback-driven optimization.

翻译：现代检索流程日益依赖查询改写和神经重排序来提升效果，但这带来了显著的计算成本，并在召回率与查询漂移之间引入根本性权衡。生成大量改写查询可大幅提升召回率，然而朴素合并或穷尽重排序其结果代价高昂。本文指出，核心挑战并非改写生成本身，而是在严格推理预算下如何自适应选择改写及其检索文档。我们提出ReformIR——一种预算感知的检索框架，将查询改写作为一等特征，借助强重排序器作为教师进行在线相关性估计。针对多个改写查询，ReformIR构建大规模候选池，并学习从改写特定检索信号中估计文档效用的轻量代理模型。在固定重排序预算下，代理模型自适应优先处理改写与文档，选择性查询以原始查询为锚定的教师重排序器。该过程通过在线特征选择机制主动抑制改写漂移，同时提升召回率。我们在MSMARCO段落语料库及TREC深度学习基准（DL19-DL22）上开展广泛实验。结果表明，ReformIR持续优于现有改写策略，尤其在改写数量增加时——先前方法因漂移导致严重质量退化。我们的发现还提示检索系统设计应转向：与其将大语言模型作为重排序器，不如在改写阶段通过反馈驱动优化更有效发挥其能力。