Paper page - Exploring Autonomous Agentic Data Engineering for Model Specialization

Source: https://huggingface.co/papers/2605.30407 Authors:

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Abstract

Large language models can autonomously execute end-to-end data engineering pipelines for model specialization through iterative data adaptation and optimization.

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-baseddata curationmethods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute anend-to-end data engineering pipelineformodel specialization. We formalizeAutonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drivemodel specializationthrough end-to-enddata curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided bypost-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-drivenmodel specializationCode will be released at https://github.com/zjunlp/DataAgent..

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