LLM System Design Interview, issue 16, Nov 14, 2025
The RoPE Misconception That Breaks Training
AI Engineer interview at Google DeepMind, and the interviewer asks:
“A new engineer implements RoPE by adding a rotational embedding to the token embeddings at the bottom of the model. The training loss is flat. What fundamental misunderstanding do they have about how and where RoPE is actually applied?”
Don’t say: “The position information is getting lost in the residual stream.”
Why adding RoPE to token embeddings silently destroys positional information — and how DeepMind engineers actually apply it inside every layer.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.