[l5870uoo9y]

> Our method, simple self-distillation (SSD), is embarrassingly simple: sample solutions from the base model with specified temperature and truncation, then fine-tune on those raw, unverified samples via standard cross-entropy loss.

So you prompt the base model for answer and then rerun the prompt with the answer from the first run?

[ACCount37]

No. There's no "answer" really.

They use self-distillation to shift the output distribution of the model towards that of the same model, but running with different temperature/truncation settings in sampling.

This effectively "folds" the logit tail truncation behavior into the model itself.

Not entirely unlike a few "model controlled sampling settings" things I've seen in what it does, but different in execution.

[3rd3]

Isn't that "scheduled sampling"? In that case they also shift the input distribution toward that of the model, which possibly is even more crucial than shifting the output distribution?

[zug_zug]

Yeah basically.

You use the outputs from the first run (right or wrong) as answers for the second training run, and repeat. Magically it works. That's what's so surprising.

I guess a theory is because there are so many diverse ways to be wrong that they don't accumulate error... still seems surprising and would be interesting to see if it works in other domains.

[yesexactly]

Yeah basically.

It's annoying as hell how much euphemistic language is used.

They say "embarassingly simple" but they really mean "something everyone already knows"

They have made 0 discoveries