Your training loss is oscillating, plateauing, or diverging. How do you debug it?
'The model won't train' comes down to a short list of usual suspects, each with a distinctive loss-curve signature. The shape of the curve names the bug before you touch a single hyperparameter. Here is how to read it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
'The model won't train' comes down to a short list of usual suspects, each with a distinctive loss-curve signature. The shape of the curve names the bug before you touch a single hyperparameter. Here is how to read it.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
