[thisismyswamp]

Playing chess & go is also search in a large tree of moves leading to particular game states

[pxeger1]

But AlphaGo etc don’t use any kind of language-based AI, so LLMs (which this thread was about) are no good.

[thisismyswamp]

The next step seems to be applying past advances in reinforcement learning with modern transformer based models

[mattsan]

Which multiple teams are working on - OpenAI (Q*), and Meta just released a reinforcement learning framework

[npsomaratna]

Could you point me towards Meta's reinforcement learning framework? I'd like to see how it stacks up against the OpenAI gym.

[mattsan]

Sure thing - https://pearlagent.github.io/

HN post here: https://news.ycombinator.com/item?id=38564526

[npsomaratna]

Thank you!

[greysphere]

The final state in chess is a single* state which yes, then branches out to N checkmate configurations and then N*M one-move-from-checkmates, and so on. (*Technically it's won/lost/draw.)

The equivalent final state in theorem proving is unique to each theorem so such a system would need to handle an additional layer-of-generalization.

[ChainOfFools]

Is this how some of the more advanced chess engines work, or even the not so advanced ones, where there's a point at which it stops searching the forward move tree in greatest depth, and instead starts searching backwards from a handful of plausible (gross move limit-bound) checkmate states looking for an intersection with a shallow forward search state?

[zone411]

Kind of, but it's calculated offline and then just accessed during the game: https://www.chessprogramming.org/Endgame_Tablebases