SPEAKER_01 argues that large models can afford to learn both domain-specific material and other capabilities because their parameter capacity is substantial.

“I mean, I think another thing is just that like it's not that expensive to do both at the same time, right? Because like the models are massive, they can easily afford in terms of their parameters to like learn everything.”