Compare distillation recipes for LLMs: hard-label SFT, on-policy logit matching, and rejection sampling.
Distillation is not a single method. The signal is knowing when to match logits versus train on generated text, why on-policy distillation beats off-policy, and how reasoning models are distilled.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Distillation is not a single method. The signal is knowing when to match logits versus train on generated text, why on-policy distillation beats off-policy, and how reasoning models are distilled.
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.
