5 ms·
I think we have ok generalized value functions (aka LLM benchmarks), but we don't have cheap approximations to them, which is what we'd need to be able to do tr
by fizx 2y ago
I think we have ok generalized value functions (aka LLM benchmarks), but we don't have cheap approximations to them, which is what we'd need to be able to do tree search at inference time. Chess works because material advantage is a pretty good approximation to winning and is trivially calculable.
- computerphage 2y agoStockfish doesn't use material advantage as an approximation to winning though. It uses a complex deep learning value function that it evaluates many times.
- alexvitkov 2y agoStill, the fact that there are obvious heuristics makes that function easier to train and and makes it presumably not need an absurd number of weights.
- bongodongobob 2y agoNo, without assigning value to pieces, the heuristics are definitely not obvious. You're taking about 20 year old chess engines or beginner projects.
- alexvitkov 2y agoEveryone understands a queen is worth more than a pawn. Even if you don't know the exact value of one piece relative to another, the rough estimate "a queen is worth five to ten pawns" is a lot better than not assigning value at all. I highly doubt even 20 year old chess engines or beginner projects value a queen and pawn the same. After that, just adding up the material on both sides, without taking into account the position of the pieces at all, is a heuristic that will correctly predict the winning player on the vast majority of all possible board positions.
- navane 2y agoHe agrees with you on the 20yr old engines and beginner projects.