Generalization Can Emerge in Tabular Foundation Models From a Single Table

• URL: http://arxiv.org/abs/2511.09665v1
• Date: Fri, 14 Nov 2025 01:03:06 GMT
• Title: Generalization Can Emerge in Tabular Foundation Models From a Single Table
• Authors: Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi, Frank Hutter, Anthony L. Caterini, Valentin Thomas,
• Abstract summary: We show that simple self-supervised pre-training on just a emphsingle real table can produce surprisingly strong transfer across heterogeneous benchmarks.<n>We then connect this to the pre-training procedure shared by most TFMs and show that the number and quality of emphtasks one can construct from a dataset is key to downstream performance.
• Score: 38.07740881271672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of $(x,y)$ pairs as context and predicts labels for new inputs without weight updates. We challenge the prevailing view that broad generalization here requires pre-training on large synthetic corpora (e.g., TabPFN priors) or a large collection of real data (e.g., TabDPT training datasets), discovering that a relatively small amount of data suffices for generalization. We find that simple self-supervised pre-training on just a \emph{single} real table can produce surprisingly strong transfer across heterogeneous benchmarks. By systematically pre-training and evaluating on many diverse datasets, we analyze what aspects of the data are most important for building a Tabular Foundation Model (TFM) generalizing across domains. We then connect this to the pre-training procedure shared by most TFMs and show that the number and quality of \emph{tasks} one can construct from a dataset is key to downstream performance.

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