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 strongest performance on both ranking (NDCG@10 +1.9\%) and retrieval (Recall@20 +0.7\%), with an average rank of 1.38 across all methods. These findings scale to Qwen3-Embedding, an LLM-based dense retriever, confirming architecture-agnostic benefits. Notably, DP reaches comparable performance in less than 50\% of the training time required by standard finetuning.

翻译：领域特定微调对密集检索器至关重要，但并非所有训练对都对学习过程有同等贡献。本文提出OPERA框架，利用训练数据异质性提升检索模型自适应的有效性与效率。我们首先研究静态剪枝方法，该方法仅保留高相似度查询-文档对，揭示出固有的质量-覆盖权衡：排序指标提升，但检索指标可能因查询多样性降低而衰减。为解决这一权衡，我们提出两阶段动态剪枝策略，在训练过程中自适应调整查询级和文档级采样概率，优先选择高质量样本同时保持对完整训练集的访问。跨六个领域的八个数据集评估验证了两种方法的有效性：静态剪枝较标准微调提升排序指标，动态剪枝在排序和检索指标上均取得最佳性能，所有方法平均排名达1.38。这些发现可扩展至基于大语言模型的密集检索器，证实其架构无关性优势。值得注意的是，动态剪枝在不到标准微调所需50%的训练时间内即可达到可比性能。