2026-06-01原文2 min readUpdated: 2026-06-01

Exploring Autonomous Agentic Data Engineering for Model Specialization

Large Language Models (LLMs) struggle to adapt to specialized domains without high-quality domain-specific data. This paper introduces Autonomous Agentic Data Engineering, where LLMs act as autonomous data engineers to plan, generate, and iteratively optimize training data. GPT-5.2 achieved a 57.29% improvement in a student model through agent-driven data adaptation.

SourcearXiv Computational LinguisticsAuthor: Yujie Luo, Xiangyuan Ru, Jingsheng Zheng, Jingjing Wang, Yuqi Zhu, Jintian Zhang, Runnan Fang, Kewei Xu, Ye Liu, Zheng Wei, Jiang Bian, Zang Li, Shumin Deng

[2605.30407] Exploring Autonomous Agentic Data Engineering for Model Specialization

[Submitted on 28 May 2026]

Title:Exploring Autonomous Agentic Data Engineering for Model Specialization

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Abstract: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 \textbf{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 \textbf{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 specialization\footnote{Code will be released at this https URL.}.

Comments: Work in progress

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)

Cite as: arXiv:2605.30407 [cs.CL]

(or arXiv:2605.30407v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.30407

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shumin Deng [view email] [v1] Thu, 28 May 2026 17:50:10 UTC (5,957 KB)

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