Engines like Stockfish might have over 100 "search parameters" that need to be tuned, to my best knowledge SPSA is preferred because the computational cost typically does not depend on the number of parameters.

Or, if attempting to use SPSA to say, perform a final post-training tune to the last layers of a neural network, this could be thousands of parameters or more.

The concern about the dimensionality of the search space is real, especially once things cross over into the 100s -- BO would certainly not be useful post-training the way the blog post talks about using SPSA.

That being said, it still seems possible to be that using a different black box optimization technique for a fairly constrained set of related magic numbers (say, fewer than 50) might lead to some real performance improvements in these systems, could be worth reaching out to the lc0 or stockfish development communities.