The user cares about the distribution of outputs. That distribution is structurally determined by the distribution of inputs (i.e. prompts), the weights of the model, and (these days) the dynamics of the harness guiding successive generations.

The only way to characterize whether a choice is 'right' is to characterize the output distribution (i.e. evals)! Changing the underlying weights necessarily invalidates whatever characterization may have been done. One may assert that one's harness regularizes outputs back toward the desirable distribution, or one may hope the different weights induce a sufficiently similar output distribution.

But no, one should not be completely agnostic to the choice of weights just because there's some nondeterminism.