What is the equivalent for batch overfitting for such a training scheme?

[tshrjn]

I've the following understanding:

The idea is to see if the Generator, when trained exclusively on the small batch, can produce samples that the Discriminator thinks are real. If your network setup and loss functions are working correctly, the Discriminator should become uncertain about whether the samples are real or fake (i.e., its loss should hover around the value indicating a 50% guess). The Generator's loss should decrease, showing it's generating better samples.

How to achieve this quickly to ensure everything in training is setup properly. I'm not getting such a behavior.