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.
Response from the author of Viridithas, there is a link to this engine in her webpage.
RivieraKidtoday at 5:58 PM
AFAIK chess is has been "solved" for a few years in the sense that Stockfish running on modern laptop with 1 minute per move is unbeatable from the starting position.
oldpersonintxtoday at 5:38 PM
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TZubiritoday at 7:44 PM
I know a fair deal about the subject of chess AI, but when I was reading this and I didn't understand. I was polarized, was I reading a mastermind that was way above my level? Or someone way too confident that learned enough buzzwords through an LLM to briefly delude someone else other than themselves?
A quick visit at the homepage suggests that it's probably the latter. I don't want to be rude, not posting out of malice, but if someone else was reading this and was trying to parse it, I think it might be helpful to compare notes and evaluate whether it's better to discard the article altogether.