I've said "we are the poor bastards who are forced to live through the learning process" before :)

> You could optimize it to keep everything the same but then you need to train more and specify all the constraints so its not practical

Though, sticking with the human brain analogy, isn't that how we learn? Could do it much faster and in parallel...

optimism

I have seen other things saying that spot training was like "labodimizing" the model and that you sacrifice competency in a specialized task for a loss in general performance. So it may be that those tasks representations where somehow correlated with each other so when you mess with one you hurt the other. You could optimize it to keep everything the same but then you need to train more and specify all the constraints so its not practical

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

carter

I have seen other things saying that spot training was like "labodimizing" the model and that you sacrifice competency in a specialized task for a loss in general performance. So it may be that those tasks representations where somehow correlated with each other so when you mess with one you hurt the other. You could optimize it to keep everything the same but then you need to train more and specify all the constraints so its not practical 