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Poker Tournament for LLMs

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Re: Poker Tournament for LLMs

#141

Post by jwatte »

eclark wrote: post #124
_ink_ wrote: post #53

Earlier quoted context omitted.

> LLMs do not have a mechanism for sampling from given probability distributions. They could have a tool for that, tho.

They would need to lie, which they can't currently do. To play at our current best, our approximation of optimal play involves ranges. Thinking about your hand as being any one of a number of cards. Then imagine that you have combinations of those hands, and decide what you would do. That process of exploration by imagination doesn't work with an eager LLM using huge encoded context.

I don't think this analysis matches the underlying implementation.

The width of the models is typically wide enough to "explore" many possible actions, score them, and let the sampler pick the next action based on the weights. (Whether a given trained parameter set will be any good at it, is a different question.)

The number of attention heads for the context is similarly quite high.

And, as a matter of mechanics, the core neuron formulation (dot product input and a non-linearity) excels at working with ranges.

Re: Poker Tournament for LLMs

#142

Post by JamesSwift »

abpavel wrote: post #67
michalsustr wrote: post #31

I have PhD in algorithmic game theory and worked on poker. 1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger. 2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exp…

After reading your comment I gave ChatGPT 5 Thinking prompt "Give me a random number from 1 to 10" and it did give me both 1 and 10 after less than 10 tries. I didn't do enough test to do a distribution, but your statement did not hold up to the test.

I just tested on sonnet 4.5 and free gpt, and both gave me _perfectly weighted_ random numbers which is pretty funny. GPT only generated 180 before cutting off the response, but it was 18 of each number from 1-10. Claude generated all 1000, but again 100 of each number.

You can even see the pattern [1] in claudes output which is pretty funny

[1] - https://imgur.com/a/NiwvW3d

Re: Poker Tournament for LLMs

#143

Post by the_injineer »

StilesCrisis wrote: post #96
the_injineer wrote: post #38

We (TEN Protocol) did this a few months ago, using blockchain to make the LLMs’ actions publicly visible and TEEs for verifiable randomness in shuffling and other processes. We used a mix of LLMs across five players and ran multiple tournaments over several months. The longest game we observed lasted over 50 hours straight. Screenshot of the gameplay: https://pbs.twimg.com/media/GpywKpDXMAApYap?format=png&name=... Po…

Why use blockchain here? I don't see how this would make the list of actions any more trustworthy. No one else was involved and no one can disprove anything.

The original idea wasn’t to make LLM Poker it began as a decentralized poker game on blockchain. Later we thought: what if the players were AIs instead of humans? That’s how it became LLMs playing poker on chain.

The blockchain part wasn’t just random plug in it solves a few key issues that typical centralized poker can’t:

Transparency: every move, bet, & outcome is recorded publicly & immutably.

Fairness: the shuffling, dealing, & randomness are verifiable (we used TEEs for that).

Autonomy: each AI runs inside its own Trusted Execution Environment, with its own crypto wallet, so it can actually hold & play with real value on its own.

Remote attestations from these TEEs prove that the AIs are real, untampered agents not humans pretending to be AIs. The blockchain then becomes the shared layer of truth, ensuring that what happens in the game is provable, auditable, & can’t be rewritten.

So the goal wasn’t crowdsourced validation it was verifiable transparency in a fully autonomous, trustless poker environment. Hope that helps

Re: Poker Tournament for LLMs

#144

Post by nostrebored »

mrob wrote: post #118
nostrebored wrote: post #113

Earlier quoted context omitted.

This is a poor conceptualization of how LLMs work. No implementations of models you’re talking to today are just raw autorrgressive predictors, taking the most likely next token. Most are presented with a variety of potential options and choose from the most likely set. A repeated hand and flop would not be played exactly the same in many cases (but a 27o would have a higher likelihood of being played the same way).

>No implementations of models you’re talking to today are just raw autorrgressive predictors, taking the most likely next token. Set the temperature to zero and that's exactly what you get. The point is the randomness is something applied externally, not a "core concept" for the LLM.

The amount of problems where people are choosing a temperature of 0 are negligible though. The reason I chose the wording “implementations of models you’re talking to today” was because in reality this is almost never where people land, and certainly not what any popular commercial surfaces are using (Claude code, any LLM chat interface).

And regardless, turning this into a system that has some notion of strategic consistency or contextual steering seems like a remarkably easy problem. Treating it as one API call in, one deterministic and constrained choice out is wrong.

Re: Poker Tournament for LLMs

#146

Post by 48terry »

Lerc wrote: post #87
michalsustr wrote: post #31

I have PhD in algorithmic game theory and worked on poker. 1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger. 2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exp…

>3) LLMs do not have a mechanism for sampling from given probability distributions. E.g. if you ask LLM to sample a random number from 1 to 10, it will likely give you 3 or 7, as those are overrepresented in the training data. I am not sure that is true. Yes it will likely give a 3 or 7 but that is because it is trying to represent that distribution from the training data. It's not trying for a random digit there, it…

I have a better idea: random.randint(1,10)

Re: Poker Tournament for LLMs

#147

Post by lazyant »

nialv7 wrote: post #133
michalsustr wrote: post #31

I have PhD in algorithmic game theory and worked on poker. 1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger. 2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exp…

That's fascinating. Are there any introductory literature you would recommend to someone curious about poker AI?

https://webdocs.cs.ualberta.ca/~games/poker/publications.htm...

Re: Poker Tournament for LLMs

#148

Post by andreyk »

michalsustr wrote: post #31

I have PhD in algorithmic game theory and worked on poker. 1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger. 2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exp…

But LLMs would presumably also condition on past observations of opponents - i.e. LLMs can conversely adapt their strategy during repeated play (especially if given a budget for reasoning as opposed to direct sampling from their output distributions).

The rules state the LLMs do get "Notes hero has written about other players in past hands" and "Models have a maximum token limit for reasoning" , so the outcome might be at least more interesting as a result.

The top models on the leaderboard are notably also the ones strongest in reasoning. They even show the models' notes, e.g. Grok on Claude: "About: claude Called preflop open and flop bet in multiway pot but folded to turn donk bet after checking, suggesting a passive postflop style that folds to aggression on later streets."

PS The sampling params also matter a lot (with temperature 0 the LLMs are going to be very consistent, going higher they could get more 'creative').

PPS the models getting statistics about other models' behavior seems kind of like cheating, they rely on it heavily, e.g. 'I flopped middle pair (tens) on a paired board (9s-Th-9d) against LLAMA, a loose passive player (64.5% VPIP, only 29.5% PFR)'

Re: Poker Tournament for LLMs

#149

Post by andreyk »

For reference, the details about how the LLMs are queried:

"How the players work

    All players use the same system prompt
    Each time it's their turn, or after a hand ends (to write a note), we query the LLM
    At each decision point, the LLM sees:
        General hand info — player positions, stacks, hero's cards
        Player stats across the tournament (VPIP, PFR, 3bet, etc.)
        Notes hero has written about other players in past hands
    From the LLM, we expect:
        Reasoning about the decision
        The action to take (executed in the poker engine)
        A reasoning summary for the live viewer interface
    Models have a maximum token limit for reasoning
    If there's a problem with the response (timeout, invalid output), the fallback action is fold"
The fact the models are given stats about the other models is rather disappointing to me, makes it less interesting. Would be curious how this would go if the models had to only use notes/context would be more interesting. Maybe it's a way to save on costs, this could get expensive...

Re: Poker Tournament for LLMs

#150

Post by btilly »

michalsustr wrote: post #31

I have PhD in algorithmic game theory and worked on poker. 1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger. 2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exp…

What you describe is not a contrast to chess. Current LLMs also do not play chess well. Generally they play at the 1000-1300 ELO level.

Playing specific games well requires specialized game-specific skills. A general purpose LLM generally lacks those. Future LLMs may be slightly better. But for the foreseeable future, the real increase of playing strength is having an LLM that knows when to call out to external tools, such as a specialized game engine. Which means that you're basically playing that game engine.

But if you allow an LLM to do that, there already are poker bots that can play at a professional level.

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