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That's what I was suggesting.
You explained to me you weren't talking about "finetuning", but training on completely synthetic data.
(Fine-tuning happens after the LLM has already been trained)
OK, yes, but that’s just semantics.
Technically pretraining and finetuning can be very similar under the hood, with the main difference being the dataset and parameters. But “training” is sometimes used interchangeably with finetuning in the hobbyist ML community.
And there’s a blurry middle ground. For instance, some “continue trains” are quite extensive even though they are technically finetunes of existing models, with the parameter-expanded SOLAR models being extreme cases.
The point I was trying to raise that wasn't semantics was that if the majority of the full training data were synthetic, it could lead to model collapse.
But luckily (or not?) a small amount of finetuning can be very effective in correcting the range of responses.