distillation
FDE interview questions tagged distillation, across every topic.
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Concepts behind "distillation"
The curriculum that explains the ideas these questions test.
Core
Synthetic Data GenerationSynthetic data uses a strong model to manufacture training or evaluation examples when human labels are scarce or expensive: instruction/response pairs, hard edge cases, distillation targets. It works when you bolt on real quality controls (dedup, filtering, diversity, verification) and fails quietly when you skip them, because you can poison your own training set and contaminate your own benchmarks.📊 Evaluation & ML FoundationsSign in
Core
Knowledge DistillationDistillation trains a small student model to mimic a large teacher, learning from the teacher's full output distribution rather than just hard labels. The soft targets carry extra signal about how the teacher 'thinks', so the student keeps much of the quality at a fraction of the size and latency. Knowing when distillation beats quantization or pruning is standard FDE ground when you have a latency or cost budget to hit.🖥️ ML Infrastructure & ServingSign in