TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen
This story is from 2026-09-28. It is preserved in the archive; the latest stories are on the live feed.
The claim behind TabPFN and TabICL: they predict on a table without ever training on it, and still beat tuned boosting. Measured on 14 datasets from the Grinsztajn benchmark, same split and same clock for everyone. The model that does not train won on 14 out of 14 against tuned XGBoost. What it cos…
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- 2026-09-28 01:01 · DEV Community — Machine Learning
TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen