A fundamental problem is as the number of parameters increase the probability of sampling from the edge of the hypercube increases. You will then not effectively explore the parameter space. This might be some what alleviated by a concentrated multivariate normal, but I guess that has its own caveat.
If you instead have a sampling algorithm informed by the loss functions you avoid this problem. (You instead might have to worry about local minima.)
If you instead have a sampling algorithm informed by the loss functions you avoid this problem. (You instead might have to worry about local minima.)