mpolson64

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.

DOE is still very useful in many contexts, but when it's possible do use a sequential design these adaptive techniques really start to pull away in terms of optimization quality.

There's simply a lot of sample efficiency to gain by adapting the experiment to incoming data in a regime where one can repeatedly design n candidates, observe their effects, and repeat m times compared to a setting where one must design a fixed experiment with n*m samples.

I'm no expert on chess engine development, but it's surprising to me that both lc0 and stockfish use SPSA for "tuning" the miscellaneous magic numbers which appear in the system rather than different black box optimization algorithms like Bayesian optimization or evolutionary algorithms. As far as I am aware both of these approaches are used more often for similar tasks in non-chess applications (ex. hyperparameter optimization in ML training) and have much more active research communities compared to SPSA.

Is there something special about these chess engines that makes SPSA more desirable for these use cases specifically? My intuition is that something like Bayesian optimization could yield stronger optimization results, and that the computational overhead of doing BO would be minimal compared to the time it takes to train and evaluate the models.