Exploring Autonomous Agentic Data Engineering for Model Specialization

• URL: http://arxiv.org/abs/2605.30407v1
• Date: Thu, 28 May 2026 17:50:10 GMT
• Title: Exploring Autonomous Agentic Data Engineering for Model Specialization
• Abstract summary: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains.<n>We formalize textbfAutonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers.<n> Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by textbf57.29%.
• Score: 37.35310287793494
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 https://github.com/zjunlp/DataAgent.}.

Related papers

• MLLM-DataEngine: Closing the Loop of Multimodal Instruction Tuning Data Generation [51.15583711678273]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation.<n>Within each loop iteration, the MLLM-DataEngine first analyzes the weakness of the model based on the evaluation results.<n>We find MLLM-DataEngine could boost the MLLMs capability in a targeted and automatic manner without human participants.
  arXiv  Detail & Related papers  (2026-07-07T08:35:38Z)
• Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders [49.87696007629591]
  Post-training data engineering largely relies on external signals and ignores rich intrinsic signals lying in model internals.<n>We propose SAERL, a data engineering framework for reinforcement learning (RL)<n>It models three intrinsic data properties: diversity, difficulty, and quality, using model internals extracted with Sparse Autoencoder (SAE), an advanced mechanistic interpretability tool.
  arXiv  Detail & Related papers  (2026-05-26T17:55:59Z)
• Learn More, Forget Less: A Gradient-Aware Data Selection Approach for LLM [51.21051698747157]
  We propose a self-adaptive gradient-aware data selection approach (GrADS) for supervised fine-tuning of large language models (LLMs)<n>Specifically, we design self-guided criteria that leverage the magnitude and statistical distribution of gradients to prioritize examples that contribute the most to the model's learning process.<n>Through extensive experimentation with various LLMs across diverse domains such as medicine, law, and finance, GrADS has demonstrated significant efficiency and cost-effectiveness.
  arXiv  Detail & Related papers  (2025-11-07T08:34:50Z)
• DeepAnalyze: Agentic Large Language Models for Autonomous Data Science [35.69385623867138]
  We introduce DeepAnalyze-8B, the first agentic designed for autonomous data science.<n>We propose a curriculum-based agentic training paradigm that emulates the learning trajectory of human data scientists.<n>We also introduce a data-grounded trajectory synthesis framework that constructs high-quality training data.
  arXiv  Detail & Related papers  (2025-10-19T15:13:42Z)
• LLM/Agent-as-Data-Analyst: A Survey [54.08761322298559]
  Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks.<n>LLMs enable complex data understanding, natural language, semantic analysis functions, and autonomous pipeline orchestration.
  arXiv  Detail & Related papers  (2025-09-28T17:31:38Z)
• LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents [3.6117068575553595]
  We introduce LaMDAgent, a framework that autonomously constructs and optimize full post-training pipelines.<n>LaMDAgent improves tool-use accuracy by 9.0 points while preserving instruction-following capabilities.<n>It uncovers effective post-training strategies that are often overlooked by conventional human-driven exploration.
  arXiv  Detail & Related papers  (2025-05-28T04:30:51Z)
• Language Models as Continuous Self-Evolving Data Engineers [32.67875951851165]
  Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks.<n>Traditional training approaches rely too much on expert-labeled data.<n>We propose a novel paradigm named LANCE that enables LLMs to train themselves by autonomously generating, cleaning, reviewing, and annotating data.
  arXiv  Detail & Related papers  (2024-12-19T18:28:41Z)
• Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud [12.651588927599441]
  We present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning.<n>These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs.<n>Experiments and an application study prove the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-12-06T09:04:12Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning [98.26836657967162]
  textbfAgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. textbfxLAM-v0.1, a large action model tailored for AI agents, demonstrates exceptional performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-02-23T18:56:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.