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arXiv · 2507.03971

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Anurag Garg·Muhammad Ali·Noah Hollmann·Lennart Purucker·Samuel Müller·Frank Hutter
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Abstract

Foundation models for tabular data, like TabPFN, achieve strong performance on small datasets when pre-trained solely on synthetic data. We show that this performance can be significantly boosted by a targeted continued pre-training phase. Specifically, we demonstrate that leveraging a small, curated collection of large, real-world datasets for continued pre-training yields superior downstream predictive accuracy compared to using broader, potentially noisier corpora like CommonCrawl or GitTables. Our resulting model, Real-TabPFN, achieves substantial performance gains on 29 datasets from the OpenML AutoML Benchmark.

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Anurag Garg, Muhammad Ali, Noah Hollmann, Lennart Purucker, Samuel Müller, Frank Hutter. 2025-07-05. Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data. https://arxiv.org/abs/2507.03971

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