Definition
Plain language
When a model learns its practice examples too closely and then does worse on anything new.
As stated in the literature
The failure mode where a model fits idiosyncrasies of the training or selection set rather than the underlying signal, degrading held-out performance; countered with regularization, held-out validation, and early stopping.
Also called: overfit, overfits
Why it matters: It matters because a model that clings to its training data looks great in testing yet fails on the new cases you actually care about.
For example, a model that memorizes every practice question aces the practice test but stumbles on the slightly different real exam.
Heard on the show
“And the SFT regression specifically might be partly a small-data overfitting artifact.”Episode 011 — When RL Actually Teaches Agents Something New, And When It Doesn't