[1]: https://hlfshell.ai/posts/deepmind-grandmaster-chess-without...
[1]: https://hlfshell.ai/posts/deepmind-grandmaster-chess-without...
It’s still very cool that they could learn a very good eval function that doesn’t require search. I would’ve liked the authors to throw out the games where the Stockfish fallback kicked in though. Even for a human, mate in 2 vs mate in 10 is the difference between a win and a draw/loss on time.
I also would’ve liked to see a head to head with limited search depth Stockfish. That would tell us approximately how much of the search tree their eval function distilled.
As for limited search tree I like the idea! I think it's tough to measure, since the time it takes to perform search across various depths vary wildly based on the complexity of the position. I feel like you would have to compile a dataset of specific positions identified to require significant depth of search to find a "good" move.
And limited depth games would not have been difficult to run. You can run a limited search Stockfish on a laptop using the UCI protocol: https://github.com/official-stockfish/Stockfish/wiki/UCI-%26...