To expand on this - an LLM will try to play (and reason) like a person would, while a solver simply crunches the possibility space for the mathematically optimal move.
It’s similar to how an LLM can sometimes play chess on a reasonably high (but not world-class) level, while Stockfish (the chess solver) can easily crush even the best human player in the world.
GTO (“game theory optimal”) poker solvers are based around a decision tree with pre-set bet sizes (eg: check, bet small, bet large, all in), which are adjusted/optimized for stack depth and position. This simplifies the problem space: including arbitrary bet sizes would make the tree vastly larger and increase computational cost exponentially.
No, I'm not super certain, but I believe most solvers are trained to be game theory optimal (GTO), which means they assume every other player is also playing GTO. This means there is no strategy which beats them in the long run, but they may not be playing the absolute best strategy.
Not only to limit the scope of what it has to simulate, but only a certain number of bet sizes is practical for a human to implement in their strategy.
How would an LLM play like a human would? I kind of doubt that there is enough recounting of poker hands or transcription of filmed poker games in the training data to imbue a human-like decision pattern.
Anybody who plays poker “optimally” is bound to lose money when they come up against anyone with skill. Once you know the strategy your opponent is employing you can play like you have anything. I believe I’ve won with 7,2 offsuite more than any other hand, because I played like I had the nuts.
This is completely wrong - the entire point of the Nash equilibrium solution (in the context of poker, at least) is that it is, at worst, EV-neutral even when your opponent has perfect knowledge of your strategy.
Your 72o comment indicates you are either playing with very weak players, or have gotten lucky, as in reasonably competitive games playing (and then full bluffing) 72o will be significantly negative EV. Try grinding that strategy at a public 10/20 table and you will be quickly butchered and sent back to the ATM.
There are numerous videos of high level professional poker players winning large hands with incredible bluffs, this whole "Nash equilibrium solution" is nothing more than a conjecture with some symbols thrown in. I will re-iterate, there is no such thing as perfect knowledge when you have imperfect information. If you play "optimally," you will get bluffed out of all your money the moment everyone else at the table figures out what you're doing.
It’s similar to how an LLM can sometimes play chess on a reasonably high (but not world-class) level, while Stockfish (the chess solver) can easily crush even the best human player in the world.