reproducibility

Temperature zero removes the sampling randomness and leaves the floating-point kind. The batch your request lands in changes the reduction order, the logits move in the last bits, and a near-tie flips a token. The fix has a cost.

A model reference that names a branch points at whatever is current, so a restart months after a deploy loads whatever the authors published since. What that produces, why the fleet ends up half-updated, and the three changes that make a deployment reproducible.

Two thousand evals against every checkpoint is ten million prompts per run and the difference between a four-hour and a two-day turnaround. The pipeline as a batch inference job with a content-addressed cache, the sharding that keeps GPUs busy, and the reproducibility rules that let a regression be believed.