What is the performance gap between TFMs pretrained on mixed synthetic-real data and those pretrained exclusiv

Abstract

Abstract: The development of tabular foundation models (TFMs) has accelerated in recent years, showing strong potential to outperform traditional ML methods for structured data. A key finding is that TFMs can be pretrained entirely on synthetic datasets, opening opportunities to design data generators that encourage desirable model properties. Prior work has mainly focused on crafting high-quality priors over generators to improve overall pretraining performance. Our insight is that parameterizing the generator distribution enables an adversarial robustness perspective: during training, we can adapt the

Research Question

What is the performance gap between TFMs pretrained on mixed synthetic-real data and those pretrained exclusively on real data, when evaluated on the TabMNAR benchmark with 30% structural missing data, and how does this gap vary with model capacity (e.g., 100M vs. 1B parameters)?

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance