Your large-model pretraining hits sudden loss spikes that don't recover. How do you stabilize it?
At billion-parameter scale, training can be running smoothly and then the loss jumps and never returns, burning a fortune in compute. The causes and the playbook are familiar to the few who've done it. Here it is.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
At billion-parameter scale, training can be running smoothly and then the loss jumps and never returns, burning a fortune in compute. The causes and the playbook are familiar to the few who've done it. Here it is.
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.
