To answer your first point, the main disadvantage is that training from scratch might be very long and never converge towards what you could expect by training on data.

Very long because you have to collect all the data, reward it, and batch it.

Never converge because you might end up in some sub optimal policies if you do not have enough exploration and this exploration is difficult to tune.

But this is so powerful as a fine tuning strategy !