Advanced Reinforcement Learning Interview Questions, issue 1, Jan 27, 2026
The Stationarity Trap
Machine Learning Engineer interview at Anthropic, and the interviewer asks:
“In Supervised Learning, we assume data is IID (Independent and Identically Distributed). Why does applying this assumption to a Reinforcement Learning agent, like a coding assistant, cause catastrophic failure?”
Assuming a fixed data distribution in RL quietly breaks learning because the agent’s behavior shifts state visitation faster than your optimizer can track.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.