For imbalanced classification, should the validation dataset be balanced?
Both test and validation datasets should have the same distribution. In such a case, the performance metrics on the validation dataset are a good approximation of the performance metrics on the test dataset. However, the training dataset can be different. Also, it is fine and sometimes helpful to balance the training dataset. On the other hand, balancing the test dataset could lead to a bias estimation from the performance of the model because the test dataset should reflect the original data imbalance.
As I mentioned at the beginning the test and validation datasets should have the same distribution. Since balancing the test dataset is not allowed, the validation dataset can not be validated too. 

Additionally, I should mention that when you balance the test dataset, you will get a better performance in comparison to using an unbalanced dataset for testing. And of course, using a balanced test set does not make sense as explained above. So, the resulted performance is not reliable unless you use an unbalanced dataset with the same distribution of classes as the actual data.