Numpy RNG non-deterministic even when seeded

I'm using numpy.random for a Monte Carlo simulation where very small acceptance/rejection probabilities are possible. Although I'm seeding the RNG, I'm unable to reproduce the same sequence of random numbers. In numpy 1.15.1's documentation it says:

Compatibility Guarantee: A fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect. Incorrect values will be fixed and the NumPy version in which the fix was made will be noted in the relevant docstring. Extension of existing parameter ranges and the addition of new parameters is allowed as long the previous behavior remains unchanged.

First of all, what do they mean by incorrect values? Second, how is roundoff error handled? Aren't values always rounded in precisely the same way? Is it possible at all that my code is not fully deterministic even though I provide a seed? I am certain that the seed is nowhere else reset because I provide my RNG object to each of my function as an argument.