As you are already doing stochastic optimization, using one sample per element per iteration is not too different from optimizing usual static inputs - every new element (random dataset row) feeds a different value to the same input “slot” anyway. It is true that you may need more epochs due to sampling.
There is an approach to generate multiple samples, also called “particles”, and process them together, like an expanded batch, but it is mainly used with probabilistic/variational modeling, where densities are used explicitly.