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Poker Tournament for LLMs

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Re: Poker Tournament for LLMs

#171

Post by AmbroseBierce »

collingreen wrote: post #104
londons_explore wrote: post #63

Earlier quoted context omitted.

They also could be funetuned for it. Eg. When asked for a random number between 1 and 10, and 3 is returned too often, you penalize that in the fine-tuning process until the distribution is exactly uniform.

RLHF for uniform numbers between 1 and 10, lol. What a world we live in now.

I get your point, but is by far the most common range humans use for random number generations on a daily basis, so its importance is kind should be expected, as well as expecting common color names have more weight than any hex representation of any of them, or just obscure names nobody uses in real life

Re: Poker Tournament for LLMs

#172

Post by dudeinhawaii »

Why are you using cutting edge models for all providers except OpenAI? Stuck out to be because I love seeing how models perform against each other on tasks. You have Sonnet 4.5 (super new) which is why it stood out when o3 is ancient (in LLM terms).

Re: Poker Tournament for LLMs

#174

Post by josh_carterPDX »

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…

Unlike chess or Go, where both players see the entire board, poker involves hidden information, your opponents’ hole cards. This makes it an incomplete-information game, which is far more complex mathematically. The AI must reason not only about what could happen, but also what might be hidden.

Even in 2-player No-Limit Hold’em, the number of possible game states is astronomically large — on the order of 10³¹ decision points. Because players can bet any amount (not just fixed options), this branching factor explodes far beyond games like chess.

Good poker requires bluffing and balancing ranges and deliberately playing suboptimally in the short term to stay unpredictable. This means an AI must learn probabilistic, non-deterministic strategies, not fixed rules. Plus, no facial cues or tells.

Humans adapt mid-game. If an AI never adjusts, a strong player could exploit it. If it does adapt, it risks being counter-exploited. Balancing this adaptivity is very difficult in uncertain environments.

Re: Poker Tournament for LLMs

#175

Post by amarant »

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 went and tested this, and asked chat gpt for a random number between 1 and 10, 4 times.

It gave me 7,3,9,2.

Both of the numbers you suggested as more likely came as the first 2 numbers. Seems you are correct!

Re: Poker Tournament for LLMs

#176

Post by lcnPylGDnU4H9OF »

amarant wrote: post #175
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 went and tested this, and asked chat gpt for a random number between 1 and 10, 4 times. It gave me 7,3,9,2. Both of the numbers you suggested as more likely came as the first 2 numbers. Seems…

I recall a video (I think it was Veritasium) which featured interviews of people specifically being asked to give a "random" number (really, the first one they think of as "random") between 1 and 50. The most common number given was 37. The video made an interesting case for why.

(It was Veritasium but it was actually a number from 1 to 100, the most common number was 7 and the most common 2-digit number was 37: https://www.youtube.com/watch?v=d6iQrh2TK98.)

Re: Poker Tournament for LLMs

#177

Post by mritchie712 »

eclark wrote: post #123

I am the author/maintainer of rs-poker ( https://github.com/elliottneilclark/rs-poker ). I've been working on algorithmic poker for quite a while. This isn't the way to do it. LLMs would need to be able to do math, lie, and be random. None of which are they currently capable. We know how to compute the best moves in poker (it's computationally challenging; the more choices and players are present, the more likely it…

> lie

LLMs are capable of lying. ChatGPT / gpt-5 is RL'd not to lie to you, but a base model RL'd to lie would happily do it.

Re: Poker Tournament for LLMs

#178

Post by godelski »

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…

  > Based on these points, it’s not technically feasible for current LLMs to play poker strongly.
To add to this a little bit it's important to note the limitations of this project. It's interesting, but I think it is probably too easy to misinterpret the results.

A few things to note:

  - It is LLMs playing against one another 
    - not against humans and not against professional humans.
    - Not an LLM being trained in poker against other LLMs (there are token limits too, so not even context) 
  - Poker is a zero sum game. 
    - Early wins can shift the course of these types of games, especially when more luck based[0][1] 
      (note: this isn't an explanation, but it is a flag. Context needed to interpret when looking at hands)
    - Lucky wins can have similar effects
  - Only one tournament. 
    Makes it hard to rule out luck issues
So important to note that it is not necessarily a good measure of a LLM's ability to play poker well, but it can to some extent tell us if the models understand the rules (I would hope so!)

But also there's some technical issues that make me suspicious... (was the site LLM generated?)

  - There's $20 extra in the grand total (assuming initial bankroll was $100k and not $100,002.22222222...)
    (This feels like a red flag...)
  - Hands 1-57 are missing?
    - Though I'm seeing "Hand #67" on the left table and "Hand #13" in the title above the associated image. But a similar thing happens for left column "Hand #58" and "Hand #63"...
  - There are pots with $0, despite there being a $30 ante...
    (Maybe I'm confused how the data is formatted? Is hand 67 a reset? There were bets pre-flop and only Grok has a flop response?)
[0] Think of it this way: we play a game of "who can flip the most heads". But we determine the number of coins we can flip by rolling some dice. If you do better on the dice roll you're more likely to do better on the coin flip.

[1] LLAMA's early loss makes it hard to come back. This wouldn't explain the dive at hand ~570. Same in reverse can be said about a few of the positive models. But we'd need to look deeper since this isn't a game of pure chance.

Re: Poker Tournament for LLMs

#179

Post by lawlessone »

godelski wrote: post #178
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…

> Based on these points, it’s not technically feasible for current LLMs to play poker strongly. To add to this a little bit it's important to note the limitations of this project. It's interesting, but I think it is probably too easy to misinterpret the results. A few things to note: - It is LLMs playing against one another - not against humans and not against professional humans. - Not an LLM being trained in poker…

I'm wondering how they relay the passage of time to the LLM? If the player just before you took 1 second or 10 seconds to make a decision that probably means something , unless they always take that amount of time.

Re: Poker Tournament for LLMs

#180

Post by RA_Fisher »

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…

LLMs can use Python to simulate from probability distributions. Though, admittedly they have to code and use their own MCMC samplers (and can’t yet utilize Stan and PyMC directly).
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