I’ve found it to help a lot with hierarchical models. So much so I might suggest we just increase the adaptation target. But that’s a different thread :-)
We need to make clear that we only need more iterations until effective sample size is high enough. If we aren’t mixing, we probably need to reparameterization rather than run more iterations.
This is all going to be relevant if @andrewgelman manages people to push the defaults down to 200 warmup and 200 adaptation or whatever he wants. Then running more iterations will often be necessary.