Issue 13, Oct 28, 2026
The Step-Function Accuracy Trap
Senior AI Engineer interview at Meta, and the interviewer asks:
“We scaled from 33B to 65B. Validation loss is clearly lower, but few-shot benchmarks barely moved, and one task got worse. Is the 65B model better? Which signal do you trust?”
Don’t say: “Trust the benchmarks. Loss is just a training number.”
Why doubling your model size can leave benchmark scores completely flat despite real learning and the continuous proxy metrics that expose hidden capability gains.
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The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.