TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14

Models Benchmarks GPU Data TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen The claim behind TabPFN and TabICL is that they predict on a table without ever training on it and still beat tuned boosting. I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone. The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without an account.

Efrain Garay 17 August 2026 18 August 2026

Listen to the summary 0:00 Playing summary The claim has been going around for months and it is concrete enough to be measurable: a tabular foundation model predicts on a table without ever having trained on it and still beats tuned boosting.