Originally published at ai.bedvibe.studio . There is an assumption almost every practitioner carries without examining it: if your dataset has bad samples in it, the loss will tell you. Corrupted rows spike. Broken files stick out. Sort by per-sample loss, look at the top of the list, and there is your garbage. I believed it too. Two separate failures in my own work say it is wrong, and they fail in the same direction — quietly. The reproducible one: a dataset that cannot b...