The cons are that RL sometimes requires more babysitting, more non-subject-matter (ie, optimization-method-specific) expertise, and can be a bit less... predict

The cons are that RL sometimes requires more babysitting, more non-subject-matter (ie, optimization-method-specific) expertise, and can be a bit less... predictable... at inference time. Also, LP often offers perfectly acceptable optimization and latency. The only case where this isn't true for offline problems is where LP times out or where relaxations of the problem are required to prevent timeout. If I had to choose one method to learn, I guess I would learn RL. But it's a false choice. Given that I know both well, I'd reach for LP unless there was a good reason to use RL instead. And from a user perspective LP is much easier to learn than RL (see cons), so I think this is good general advice. Definitely worth having both tools on your belt if you plan on working on problems where either is a good fit.