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New agentic system automates pre-training data selection for LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AutoData, an agentic system designed to automate the selection of pre-training data for large language models. This agent treats data selection as a heuristic engineering problem, searching directly over executable algorithms that utilize document features like lexical statistics and perplexity. AutoData iteratively refines these algorithms based on validation feedback from a proxy model, discovering a data selection recipe that surpasses human-designed curation pipelines within a single night of searching. The discovered method also demonstrates effectiveness at larger scales, improving downstream metrics. AI

IMPACT Automates a critical, labor-intensive part of LLM development, potentially accelerating research and deployment.

RANK_REASON The item is a research paper detailing a new method for data selection in LLM pre-training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New agentic system automates pre-training data selection for LLMs

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The item is a research paper detailing a new method for data selection in LLM pre-training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yan Meng, Dhruv Srikanth, Bingchen Zhao, Zhengyao Jiang, Yuxiang Wu ·

    AutoData: Agentic Search for Pre-training Data Selection

    arXiv:2609.19754v1 Announce Type: new Abstract: LLM agents have recently shown promise in automating machine learning engineering by editing model and training code under execution feedback. Data, however, remains largely outside this agentic optimisation loop. We frame pre-train…

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