A Comprehensive Approach to Instruction Tuning for Qwen2.5: Data Selection, Domain Interaction, and Training Protocols

Instruction tuning plays a pivotal role in aligning large language models with diverse tasks, yet its effectiveness hinges on the interplay of data quality, domain composition, and training strategies. This study moves beyond qualitative assessment to systematically quantify these factors through extensive experiments on data selection, data mixture, and training protocols. By quantifying performance trade-offs, we demonstrate that the implicit method SuperFiltering achieves an optimal balance, whereas explicit filters can induce capability conflicts. A fine-grained analysis of cross-domain interactions quantifies a near-linear competition between code and math, while showing that tool use data exhibits minimal interference. To mitigate these measured conflicts, we compare multi-task, sequential, and multi-stage training strategies, revealing that multi-stage training significantly reduces Conflict Rates while preserving domain expertise. Our findings culminate in a unified framework for optimizing instruction tuning, offering actionable, data-driven guidelines for balancing multi-domain performance and enhancing model generalization, thus advancing the field by providing a methodology to move from intuition to systematic optimization.