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Poker Tournament for LLMs

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Re: Poker Tournament for LLMs

#61

Post by HenryBemis »

oldestofsports wrote: post #54
gsinclair wrote: post #36

Earlier quoted context omitted.

FWIW, I’d bet some coin that current CharGPT would provide a genuine pseudo-random number on request. It now has the ability to recognise when answering the prompt requires a standard algorithm instead of ordinary sentence generation. I found this out recently when I asked it to generate some anagrams for me. Then I asked how it did it.

I asked chatgpt for a ramdom number between 1 and 10. It answered 7, then i asked for anpther, and it answered 3.

I asked Gemini and it gave me 8 and then I asked again and it gave me 9.

Re: Poker Tournament for LLMs

#62

Post by apt-apt-apt-apt »

Sweepi wrote: post #49

Imo, this shows that LLMs are nice for compression, OCR and other similar tasks, but there is 0% thinking / logic involved: magistral: "Turn card pairs the board with a T, potentially completing some straights and giving opponents possible two-pair or better hands" A card which pairs the board does not help with straights. The opposite is true. Far worse then hallucinating a function signature which does not exist, i…

It never claimed that pairing the board helps with straights, only that some straights were potentially completed.

Ironically, the example you gave in your point was based on a fundamental misinterpretation error, which itself was about basing things on fundamental errors.

Re: Poker Tournament for LLMs

#63

Post by londons_explore »

_ink_ wrote: post #53
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 do not have a mechanism for sampling from given probability distributions. They could have a tool for that, tho.

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.

Re: Poker Tournament for LLMs

#64

Post by Cool_Caribou »

bluecalm wrote: post #48
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…

>>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. It's not that the algorithm is currently not known but it's the nature of the game that deterministic equilibrium strategies don't exist for anything but most trivial games. It's very easy to prove as well (think Rock-Paper-Sci…

Is limit poker a trivial game? I believe it's been solved for a long time already.

Re: Poker Tournament for LLMs

#65

Post by pablorodriper »

I gave a talk on this topic at PyConEs just 10 days ago. The idea was to have each (human) player secretly write a prompt, then use the same model to see which one wins.

It’s just a proof of concept, but the code and instructions are here: https://github.com/pablorodriper/poker_with_agents_PyConEs20...

Re: Poker Tournament for LLMs

#66

Post by imperfectfourth »

crackpype wrote: post #52

It seems to be broken? For example in this hand, the hand finishes at the turn even though 2 players still live. https://pokerbattle.ai/hand-history?session=37640dc1-00b1-4f...

one of them went all in, but still the river should have opened because none of them are drawing dead. Kc is still in deck which will make llama the winning hand(other players have the other two kings). If it was Ks instead in the deck, llama would be drawing dead because kimi would improve to a flush even if king opened.

Re: Poker Tournament for LLMs

#67

Post by abpavel »

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.

Re: Poker Tournament for LLMs

#68

Post by hadeson »

bluecalm wrote: post #48
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…

>>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. It's not that the algorithm is currently not known but it's the nature of the game that deterministic equilibrium strategies don't exist for anything but most trivial games. It's very easy to prove as well (think Rock-Paper-Sci…

I don't think it's easier, a bad poker bot will lose a lot over a large enough sample size. But maybe it's easier to incorporate exploitation into your strategy - exploits that rely more on human psychology than pure statistics?

Re: Poker Tournament for LLMs

#69

Post by bluecalm »

Cool_Caribou wrote: post #64
bluecalm wrote: post #48

Earlier quoted context omitted.

>>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. It's not that the algorithm is currently not known but it's the nature of the game that deterministic equilibrium strategies don't exist for anything but most trivial games. It's very easy to prove as well (think Rock-Paper-Sci…

Is limit poker a trivial game? I believe it's been solved for a long time already.

>>Is limit poker a trivial game? I believe it's been solved for a long time already.

It's definitely not trivial. Solving it (or rather approximating the solution close enough to 0) was a big achievement. It also doesn't have a deterministic solution. A lot of actions in the solution are mixed.

Re: Poker Tournament for LLMs

#70

Post by michalsustr »

bluecalm wrote: post #48
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…

>>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. It's not that the algorithm is currently not known but it's the nature of the game that deterministic equilibrium strategies don't exist for anything but most trivial games. It's very easy to prove as well (think Rock-Paper-Sci…

> It's not that the algorithm is currently not known but it's the nature of the game that deterministic equilibrium strategies don't exist for anything but most trivial games.

Thanks for making this more precise. Generally for imperfect-information games, I agree it's unlikely to have deterministic equilibrium, and I tend to agree in the case of poker -- but I recall there was some paper that showed you can get something like 98% of equilibrium utility in poker subgames, which could make deterministic strategy practical. (Can't find the paper now.)

> I have no idea what you mean by "online search"

Continual resolving done in DeepStack [1]

> or "mechanism to ensure strategy consistency"

Gadget game introduced in [3], used in continual resolving.

> "it's likely mixed between call and a fold"

Being imprecise like this would arguably not result in a super-human play.

> Adding some form of RNG to LLM is trivial as well and already often done (temperature etc.)

But this is in token space. I'd be curious to see a demonstration of sampling of a distribution (i.e. some uniform) in the "token space", not via external tool calling. Can you make an LLM sample an integer from 1 to 10, or from any other interval, e.g. 223 to 566, without an external tool?

> You can have as much training data for poker as you have for chess. Just use a very strong program that approximates the equilibrium and generate it.

You don't need an LLM under such scheme -- you can do a k-NN or some other simple approximation. But any strategy/value approximation would encounter the very same problem DeepStack had to solve with gadget games about strategy inconsistency [5]. During play, you will enter a subgame which is not covered by your training data very quickly, as poker has ~10^160 states.

> The reason both games are hard for LLMs is that they require precision and LLMs are very bad at precision.

How you define "precision" ?

> I am not sure which game is easier to teach an LLM to play well. I would guess poker.

My guess is Chess, because there is more training data and you do not need to construct gadget games or do ReBeL-style randomizations [4] to ensure strategy consistency [5].

[3] https://arxiv.org/pdf/1303.4441

[4] https://dl.acm.org/doi/pdf/10.5555/3495724.3497155

[5] https://arxiv.org/pdf/2006.08740

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