# 探索面向模型专化的自主智能体数据工程

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-28 08:00
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmpulkg6204vfslag9r6ubiil
- 原文链接：https://arxiv.org/abs/2605.30407

## AI 摘要

大语言模型在通用任务上表现强劲，但在适应专业领域时往往缺乏高质量领域数据。现有方法依赖人工设计的工作流，本研究正式提出“自主智能体数据工程”这一新任务，旨在评估LLM作为自主数据工程师执行端到端数据整理流程以实现模型专化的能力。实验表明，GPT-5.2作为自主数据工程师，通过智能体驱动的迭代数据适配，构建训练课程使学生模型性能提升57.29%。研究将自主数据工程确立为一种可量化的能力，为智能体驱动的模型专化指明了路径，代码将于 https://github.com/zjunlp/DataAgent 发布。

## 正文

Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided by post-training performance improvement. Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by 57.29\%, entirely through iterative, agent-driven data adaptation. By illuminating both potential and bottlenecks, our study establishes autonomous data engineering as a measurable capability and charts a path toward agent-driven model specializationCode will be released at https://github.com/zjunlp/DataAgent..