Even though you have lumped the three in one discussion, I view RL, wavelets and guided filtering as apples and oranges. We have already established that RL does well in linear, so I will let that go for now.
To me, wavelets are their own domain, just as there is a Fourier domain and a gradient one, etc. As long as I get to recompose and return to the original domain, all is well. Of course, it might be simpler to start from linear, as transforming one too many times may introduce discontinuities or artifacts that would shock those of us who would care about those things.
Then there is guided filtering. It and similar filters are interested in smoothing flat areas while preserving edges. Let me put it this way, what would you consider an edge? In linear space, the edges would be focused in the brightest regions and the rest would be regularized; in most cases, that is not what we want. Hence, why I linked @Carmelo_DrRaw’s work. There might be objections to the implementation details, but the idea that you need to find the edges remain.