智能体式指令数据选择：让DataMaster解读你的意图
Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent
AI总结：
该研究提出指令数据选择智能体DataMaster，可解读用户意图自动构建最优数据选择策略，在多领域实验中其性能优于静态基线及全池训练。
AI中文摘要：
尽管现有的指令数据选择方法已引入多种指标，但真实世界数据集的固有复杂性使得任何单一指标都无法在所有场景中通用。因此，开发者往往被迫手动检查数据并为每个新应用设计启发式规则——这一过程繁琐且易出错。本文中，我们提出从手动配置转向通过指令数据选择智能体（DataMaster）实现自动编排的范式转变，该智能体可解读用户意图并自主构建最优选择策略。通过允许用户以自然语言描述指定数据需求，DataMaster简化了数据整理工作并消除了手动设计策略的负担。在数学、医学和代码领域开展的大量实验表明，DataMaster在多数设置下优于静态基线，且在相当多的案例中超过全池训练。DataMaster的实现及复现所报告流程所需的脚本已公开，网址为this https URL。
英文摘要：
Although existing instruction data selection methods have introduced various metrics, the inherent complexity of real-world datasets makes it impractical for any single metric to generalize across all scenarios. Developers are thus often forced to manually inspect data and craft heuristic rules for each new application---a tedious and error-prone process. In this paper, we propose a paradigm shift from manual configuration to automated orchestration via the Instruction Data Selection Agent (DataMaster), which interprets user intent and autonomously composes optimal selection strategies. By allowing users to specify data needs through natural language descriptions, DataMaster simplifies data curation and removes the burden of manual strategy design. Extensive experiments across the math, medical, and code domains show that DataMaster outperforms static baselines in most settings and surpasses full-pool training in a substantial number of cases. The implementation of DataMaster and the scripts needed to reproduce the reported pipeline are publicly available at https://github.com/nju-websoft/DataMaster.
