Advanced Reinforcement Learning Interview Questions, issue 2, Jan 28, 2026
The Mean Collapse Trap
Machine Learning Engineer interview at Tesla, and the interviewer asks:
“We have an imitation learning agent that is underfitting complex human driving data. A junior engineer suggests scaling the backbone network size by 10x to 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘦 𝘤𝘢𝘱𝘢𝘤𝘪𝘵𝘺. We are currently using a simple Gaussian output head. Why will scaling the network fail to solve the problem, no matter how much compute you throw at it?”
You can spend infinite compute learning better parameters, but a Gaussian head still cannot represent forks in the action space.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.