In linear compressed sensing, complexity tuning doesn't improve reconstruction error, so practical gains from tunable generative priors likely depend on the nonlinear structure of neural networks rather than the tuning mechanism itself.
This paper proves that for linear generative priors in noiseless compressed sensing, using the full-complexity model always minimizes reconstruction error—simpler priors don't help.