DataFoundry: Evolving Data Preparators via Recursive Self-Improvement
- URL: http://arxiv.org/abs/2608.29966v1
- Date: Sun, 30 Aug 2026 18:53:24 GMT
- Title: DataFoundry: Evolving Data Preparators via Recursive Self-Improvement
- Abstract summary: textscDataFoundry represents a data preparator as an evolvable runtime specification and instantiates its evolution with a textsc-as-Modules architecture.<n>We evaluate textscDataFoundry on DataPrep-Bench across mathematics, finance, law, and medicine.
- Score: 34.053745857869636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Domain adaptation of large language models increasingly depends on constructing high-quality training data, yet existing data-preparation pipelines typically address quality only after generation through post-hoc filtering. This creates a fundamental mismatch: data-quality issues often originate from the construction process itself, while quality control is applied only to its outputs. We introduce \textsc{DataFoundry}, a framework for \textbf{evolving data preparators through recursive self-improvement} before large-scale data production. \textsc{DataFoundry} represents a data preparator as an evolvable runtime specification and instantiates its evolution with a \textsc{Skills-as-Modules} architecture, in which a central \textsc{Controller} orchestrates modular skills to compile executable runtimes, diagnose deficiencies on small pilot sets using domain-appropriate criteria, and translate diagnostic feedback into adapters that revise individual preparation components while preserving stable interfaces. We evaluate \textsc{DataFoundry} on DataPrep-Bench across mathematics, finance, law, and medicine, and find that recursively evolved preparators produce training data with higher downstream utility than baselines. Experiments across different backbones further demonstrate that these improvements are not tied to a particular model, while analyses and case studies further reveal the framework's optimization dynamics and illustrate how its evolution unfolds in practice.
Related papers
- DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving [55.85201665674226]
High-quality training data is essential to large language models (LLMs)<n>Existing automatic data preparation methods rely on predefined pipelines or customized human instructions.<n>We introduce DataEvolver, the first self-evolving data preparation system that automatically constructs pipelines to transform raw data into high-quality data.
arXiv Detail & Related papers (2026-06-05T07:44:34Z) - Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora [33.535457603064906]
We show that a structured knowledge representation extracted from the source corpus serves as the shared foundation for both training data and evaluation.<n>This work establishes a principled foundation for the reliable engineering of human expertise into language models.
arXiv Detail & Related papers (2026-04-27T14:05:18Z) - Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs [81.43302841109349]
Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs)<n>We propose an automated multi-agent framework to reconstruct the evolutionary graph of dataset development.
arXiv Detail & Related papers (2026-04-12T06:24:07Z) - LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domain [4.161364556213409]
This paper presents an experience in integrating and retrieving qualification data for electronic components used in satellite board design.<n>We propose a pipeline that uses Virtual Knowledge Graphs to enhance retrieval and reduce manual effort in data cleansing.
arXiv Detail & Related papers (2026-03-20T16:22:06Z) - Agentic Planning with Reasoning for Image Styling via Offline RL [66.10749901925941]
Direct prompt-based editing fails on complex transformations because vague and subjective prompts often require nuanced understanding of what should be changed in the image.<n>We present a tool-based agentic RL post-training framework that addresses this through structured planning with chain-of-thought reasoning.
arXiv Detail & Related papers (2026-03-07T11:14:37Z) - Generative Data Transformation: From Mixed to Unified Data [57.84692191369066]
textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
arXiv Detail & Related papers (2026-02-26T08:30:09Z) - Data Complexity-aware Deep Model Performance Forecasting [0.5735035463793009]
We propose a lightweight, two-stage framework that can estimate model performance before training.<n>The setup allows the framework to generalize across datasets and model types.<n>We find that some of the underlying features used for prediction can offer practical guidance for model selection.
arXiv Detail & Related papers (2026-01-04T05:31:04Z) - OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value [74.80873109856563]
OpenDataArena (ODA) is a holistic and open platform designed to benchmark the intrinsic value of post-training data.<n>ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; and (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources.
arXiv Detail & Related papers (2025-12-16T03:33:24Z) - An Integrated Data Processing Framework for Pretraining Foundation Models [57.47845148721817]
Researchers and practitioners often have to manually curate datasets from difference sources.
We propose a data processing framework that integrates a Processing Module and an Analyzing Module.
The proposed framework is easy to use and highly flexible.
arXiv Detail & Related papers (2024-02-26T07:22:51Z) - Faithful Low-Resource Data-to-Text Generation through Cycle Training [14.375070014155817]
Methods to generate text from structured data have advanced significantly in recent years.
Cycle training uses two models which are inverses of each other.
We show that cycle training achieves nearly the same performance as fully supervised approaches.
arXiv Detail & Related papers (2023-05-24T06:44:42Z)
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.