I think we're saying the same thing? I said they are explicitly trained on these tasks, not that they are some separate models during programming RL, or business tasks RL.

> The cool thing is that training on diverse datasets improves scores on other downstream tasks, while the training objective is the same.

Maybe for some language modelling tasks, i.e. it learns some internal representation that is transferable. However I would find it quite odd if a model becomes good at Bio while not explicitly going through Bio training.

> I said they are explicitly trained on these tasks, not that they are some separate models during programming RL, or business tasks RL.

You said:

> > nor are they trained for each task individually.

> They are explicitly trained for each task individually.

And that's the main misunderstanding.

Collins says:

individually in American English (ˌɪndəˈvɪdʒuəli, ˌɪndəˈvɪdʒəli) adverb 1. as an individual or individuals rather than as a group; one at a time; separately; singly

Which is precisely what LMs don't do (in contrast to previous "AI" models, which did do that). They are trained on every datapoint at the same time. So long as we agree on that, I think we are saying the same thing :)