Implement an exponential moving average (EMA) of model weights, and explain why it helps.
Many state-of-the-art training runs hold a shadow copy of the weights that averages the trajectory, then serve that rather than the final step. It's a few lines and a genuine accuracy gain. 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.
Many state-of-the-art training runs hold a shadow copy of the weights that averages the trajectory, then serve that rather than the final step. It's a few lines and a genuine accuracy gain. 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.
