The next step seems to be applying past advances in reinforcement learning with modern transformer based models

Is this how some of the more advanced chess engines work, or even the not so advanced ones, where there's a point at which it stops searching the forward move tree in greatest depth, and instead starts searching backwards from a handful of plausible (gross move limit-bound) checkmate states looking for an intersection with a shallow forward search state?