Advanced Deep Learning Interview Questions #8 - The False Convergence Trap

Shrinking parameter updates often reflect a dying learning rate, not actual convergence, causing pipelines to halt while gradients are still active.

You’re in a Senior Machine Learning Engineer interview at OpenAI. The interviewer sets a trap:

“Your automated training pipeline monitors the distance between successive parameter updates. It halts training when the distance between steps drops below 1e^{-5}, flagging the model as ‘converged.’ But in production, the model’s accuracy is absolute garbage. …