Knowledge Distillation
Knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight.
Definition
Knowledge Distillation refers to knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what knowledge distillation is saying. If knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Knowledge Distillation: what would falsify the current reading in the next window?