I'm not an expert, but as I understand it there are existing solvers for poker/holdem? Perhaps one of the players could be a traditional solver to see how the LLMs fare against those?

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projectyang 15 hours ago | parent | next [-]

While others have commented about solvers, I'd also like to bring up AI poker bots such as Pluribus (https://en.wikipedia.org/wiki/Pluribus_(poker_bot)).

This also wouldn't even be a close contest, I think Pluribus demonstrated a solid win rate against professional players in a test.

As I was developing this project, a main thought came to mind as to the comparison between cost and performance between a "purpose" built AI such as Pluribus versus a general LLM model. I think Pluribus training costs ~$144 in cloud computing credits.

	▲	darepublic 2 hours ago \| parent [-]
		Should be noted that this bot is heads up only? I believe a form of heads up poker is effectively solved as well-- limit hold'em heads up

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lowbatt a day ago | parent | prev | next [-]

the LLMs would get crushed

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cowthulhu a day ago | parent [-]

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.

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postpriorx a day ago | parent | next [-]

How does a poker solver select bet size? Doesn't this depend on posteriors on the opponent's 'policy' + hand estimation?

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Reason077 13 hours ago | parent | next [-]

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.

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boscillator a day ago | parent | prev | next [-]

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.

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iberator 11 hours ago | parent | prev | next [-]

Nash equilibrium. Optimal strategy for online poker has been known for like literally 20 years right now

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sejje a day ago | parent | prev [-]

Typically when you run a simulation on a hand, you give it some bet size options.

To limit the scope of what it has to simulate.

It's unlikely they're perfect, but there's very small differences in EV betting 100% vs 101.6% or whatever.

	▲	meep_morp 21 hours ago \| parent [-]
		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.

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bogzz a day ago | parent | prev | next [-]

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.

	▲	Terr_ 11 hours ago \| parent \| next [-]
		Also, if you set the bar for human players low enough, pretty much any set of actions is human-like. :p
	▲	meep_morp 21 hours ago \| parent \| prev [-]
		I don't have an answer, but there's over a decade of hand history discussions online from various poker forums like 2p2 and more recently Reddit.

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FergusArgyll a day ago | parent | prev | next [-]

You are of course correct but to be pedantic:

Stockfish isn't really a solver it's a neural net based engine

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DiscourseFan a day ago | parent | prev [-]

Unlike Chess, in poker you don’t have perfect information, so there’s no real way to optimize it.

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tim-kt a day ago | parent [-]

You can still optimize for the expectation value, which is also essentially poker strategy.

	▲	DiscourseFan an hour ago \| parent [-]
		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.

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sejje a day ago | parent | prev [-]

The solvers don't typically work in real time, I don't think. They take a while to crunch a hand.

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dmurray a day ago | parent [-]

"Solvers" normally means algorithms which aim to produce some mathematically optimal (given certain assumptions) behaviour.

There are other poker playing programs [0] - what we called AI before large language models were a thing - which achieve superhuman performance in real time in this format. They would crush the LLMs here. I don't know what's publicly available though.

[0] e.g. https://en.wikipedia.org/wiki/Pluribus_(poker_bot)

	▲	sejje 11 hours ago \| parent [-]
		Solvers, in a poker context, are a category of programs. They run a simulation after you enter the known information. Like piosolver, as an example. The best poker-playing AI is not beatable by anyone, so yes, it would crush the LLMs.