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

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.

External IDs:dblp:journals/corr/abs-2507-03971