Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, andEarly Stopping

Why teaching language AI can act different each time — and what to do

We tried teaching the same language model to do new jobs over and over, and found results that jump around a lot.
Even with the same settings, tiny differences cause big swings, because of randomness in how the model starts and how the examples are shown.
Two things matter most: the first choice of the model's numbers, called weight initialization, and the order you feed the training examples, called data order.
Both push outcomes up or down about the same amount.
On small task sets many attempts start okay but then diverges mid-training, wasting time.

So you can run many quick tries, watch the score, and use early stopping to kill runs that won't win; that saves time and compute.
We also put all our logs and scores out in the open so others can dig in, learn and repeat — the full results are public.
Try more short runs, stop the bad ones, and you may find much better models than you expect.

Read article comprehensive review in Paperium.net:
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, andEarly Stopping

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