I think there are some reasonable design considerations here,
not obvious what the best answer(s) is/are.
1. one way or the other we should prevent users from running
anova() on REML-fitted models *with different fixed effects* (arguably
there is a sensible use case for using anova() on models that
differ only in their random effects
1a. should we try to do this automatically or should we throw
an error and hint to them how best to achieve their goal?
2 refit should allow appropriate optimization control parameters to be set
2a a model refit should always inherit the optimization control parameters
from its parent model
3 we should try not to make too many backward-incompatible changes
(i.e. even if the current behaviour is weird we should make changes
carefully/gradually so as not to disturb users who have adapted to it)