Fine-Tuning
Fine-tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain.
Definition
Fine-Tuning refers to tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain. 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 tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain 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 fine-tuning is saying. If tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain 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 Fine-Tuning: what would falsify the current reading in the next window?