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Core
Knowledge Distillation
Knowledge distillation trains a small student model to imitate a larger teacher, using the teacher's soft probability distribution (or internal features) as a richer training signal than hard labels. A student trained this way typically beats an identical model trained from scratch on the same data, because the soft targets encode the teacher's learned similarity structure. Applied AI interviews probe it because it is the main lever for shrinking a capable model into something cheap to serve, and because reasoning distillation and the legal terms around teacher outputs are live issues in 2026.
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ML Infrastructure & GPUsExplain knowledge distillation: how it works, when to use it, and how it compares to quantization and pruning.→LLM & GenAI FundamentalsHow do you distill a large LLM into a smaller one, and what are the approaches?→LLM & GenAI FundamentalsCompare distillation recipes for LLMs: hard-label SFT, on-policy logit matching, and rejection sampling.→
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