Issue 10, Oct 24, 2026
The Absolute Position Cliff
Senior ML Engineer interview at Google DeepMind, and the interviewer asks:
“Your model was trained with learned absolute positional embeddings at a 4K context. Product needs 16K for long agent traces. Someone wants to resize the position table and ship. What breaks at position 4,097, and why isn’t ‘just fine-tune on longer data’ the whole answer?”
Don’t say: “The model hasn’t seen those positions, so fine-tune on 16K data and it’ll learn them.”
Why extending a 4K lookup table turns token #4,097 into random noise and why fine-tuning alone can't save your attention mechanism from collapsing.
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The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.