R-Select: A Robust Multi-Metric Data Selection Approach for Fine-Tuning Large Language Models

Abstract

The transition from architecture-centric scaling to data-centric refinement has established high-quality data as a critical determinant of Large Language Model performance, particularly for complex reasoning and instruction following. However, effective data selection remains a persistent bottleneck: simple heuristic filters often fail to capture multifaceted data features (e.g., reasoning depth and diversity), while advanced model-based scoring methods typically prioritize isolated quality dimensions, failing to provide a holistic assessment. To address these challenges, we propose R-Select, a robust and scalable framework that optimizes data selection with 30 distinct quality metrics. Recognizing that optimizing such a high-dimensional feature space is non-trivial, R-Select introduces a novel hierarchical optimization strategy. This approach structurally decomposes the search problem by first clustering correlated metrics into functional groups based on statistical dependencies. It then executes a two-stage optimization process: performing intra-group refinement to maximize local representational power, followed by inter-group integration to balance global quality domains. Crucially, to ensure computational efficiency, R-Select employs a low-resource proxy strategy, utilizing a lightweight model on a small data subset to learn an optimal selection policy that is transferable to the target model. Extensive experiments demonstrate that R-Select consistently outperforms both heuristic baselines and model-based methods, offering a robust solution for high-quality data curation.