[2603.17205] OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

Computer Science > Information Retrieval arXiv:2603.17205 (cs) [Submitted on 17 Mar 2026 (v1), last revised 1 Apr 2026 (this version, v2)] Title:OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Authors:Haoyang Fang, Shuai Zhang, Yifei Ma, Hengyi Wang, Cuixiong Hu, Katrin Kirchhoff, Bernie Wang, George Karypis View a PDF of the paper titled OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation, by Haoyang Fang and 7 other authors View PDF HTML (experimental) Abstract:Domain-specific finetuning is essential for dense retrievers, yet not all training pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits this heterogeneity to improve both the effectiveness and efficiency of retrieval model adaptation. We first investigate static pruning (SP), which retains only high-similarity query-document pairs, revealing an intrinsic quality-coverage tradeoff: ranking (NDCG) improves while retrieval (Recall) can degrade due to reduced query diversity. To resolve this tradeoff, we propose a two-stage dynamic pruning (DP) strategy that adaptively modulates sampling probabilities at both query and document levels throughout training, prioritizing high-quality examples while maintaining access to the full training set. Evaluations across eight datasets spanning six domains demonstrate the effectiveness of both approaches: SP improves ranking over standard finetuning (NDCG@10 +0.5\%), while DP achieves the s...