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Poker Tournament for LLMs

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Re: Poker Tournament for LLMs

#71

Post by Sweepi »

apt-apt-apt-apt wrote: post #62
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.

?? It says that "Turn card pairs the board" (correct!) which means that there already was a ten(T), and now there is a 2nd ten(T) on the board aka in the community cards.

Obviously, a card that pairs the board does not introduce a new value to the community cards and therefore can not complete or even help with any straight.

What error are you talking about?

Re: Poker Tournament for LLMs

#72

Post by apt-apt-apt-apt »

Sweepi wrote: post #71
apt-apt-apt-apt wrote: post #62

Earlier quoted context omitted.

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.

?? It says that "Turn card pairs the board" (correct!) which means that there already was a ten(T), and now there is a 2nd ten(T) on the board aka in the community cards. Obviously, a card that pairs the board does not introduce a new value to the community cards and therefore can not complete or even help with any straight. What error are you talking about?

Oops, you're right. I didn't think it through enough.

Re: Poker Tournament for LLMs

#73

Post by michalsustr »

joelthelion wrote: post #51
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 interesting, because you show a fundamental limitation of current LLMs in which there is a skill that humans can learn and that LLMs cannot currently emulate. I wonder if there are people working on closing that gap.

Humans are very bad at random number generation as well.

LLMs can do sampling via external tools, but as I wrote in other thread, they can't do this 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?

Re: Poker Tournament for LLMs

#74

Post by x______________ »

gsinclair wrote: post #36
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…

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.

Depends on how you ask it if course. Chatgpt:

  Output the random generation of a number between 1 and 10, 100 times
..chatgpt would only provide me with a python script and then offered to add scrolling numbers and colourful digits.

Tried again in a new session with:

  Generate a random number between 1 and 10, 100 times. Output only
4, 9, 1, 6, 10, 3, 2, 5, 7, 8, 1, 9, 4, 6, 10, 2, 8, 5, 7, 3, 9, 1, 4, 6, 10, 8, 2, 7, 3, 5, 1, 9, 6, 10, 2, 4, 8, 3, 7, 5, 9, 1, 10, 4, 6, 2, 8, 3, 5, 7, 9, 10, 4, 6, 1, 2, 8, 3, 5, 7, 9, 1, 10, 6, 4, 2, 8, 5, 7, 3, 9, 10, 6, 4, 2, 1, 8, 5, 7, 3, 9, 10, 4, 6, 2, 8, 5, 7, 3, 9, 1, 10, 4, 6, 8, 2, 5, 7, 3, 9

Notice the issue?

Re: Poker Tournament for LLMs

#75

Post by michalsustr »

nabla9 wrote: post #56
michalsustr wrote: post #47

Earlier quoted context omitted.

Agreed. I don't know how fast it would get into the money, but an equilibrium strategy is guaranteed to not lose, in expectation. So as long as the variance doesn't make it to run out of money, over the long run it should collect most of the money in the game. It would be fun to try!

> equilibrium strategy is guaranteed to not lose, In my scenario and tournament play. Are you sure? I would be shocked to learn that there is a Nash equilibrium in multi-player setting, or any kind of strategic stability.

In multi-player you don't have guarantees, but it tends to work well anyway: https://www.science.org/doi/full/10.1126/science.aay2400

Re: Poker Tournament for LLMs

#76

Post by joelthelion »

michalsustr wrote: post #73
joelthelion wrote: post #51

Earlier quoted context omitted.

That's interesting, because you show a fundamental limitation of current LLMs in which there is a skill that humans can learn and that LLMs cannot currently emulate. I wonder if there are people working on closing that gap.

Humans are very bad at random number generation as well. LLMs can do sampling via external tools, but as I wrote in other thread, they can't do this 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 exte…

They can learn though. Humans can get decent at poker.

Re: Poker Tournament for LLMs

#77

Post by x______________ »

x______________ wrote: post #74
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.

Depends on how you ask it if course. Chatgpt: Output the random generation of a number between 1 and 10, 100 times ..chatgpt would only provide me with a python script and then offered to add scrolling numbers and colourful digits. Tried again in a new session with: Generate a random number between 1 and 10, 100 times. Output only 4, 9, 1, 6, 10, 3, 2, 5, 7, 8, 1, 9, 4, 6, 10, 2, 8, 5, 7, 3, 9, 1, 4, 6, 10, 8, 2, 7,…

Asked again:

  Output the random generation of a number between 1 and 10, 100 times
Sorry python is not available, run it locally with offered code.

Immediately:

  Output the random generation of a number between 1 and 10, 100 times
Sure!

[5, 2, 7, 1, 10, 4, 9, 8, 3, 6, 2, 5, 9, 7, 1, 10, 4, 8, 3, 6, 7, 3, 9, 2, 5, 8, 10, 1, 4, 6, 8, 4, 6, 10, 9, 7, 1, 5, 3, 2, 9, 10, 3, 1, 7, 2, 4, 8, 6, 5, 3, 8, 5, 2, 10, 9, 6, 1, 4, 7, 4, 9, 8, 6, 1, 3, 7, 2, 10, 5, 6, 7, 5, 9, 3, 10, 8, 2, 1, 4, 1, 3, 8, 5, 2, 6, 9, 4, 10, 7, 10, 5, 2, 9, 8, 3, 7, 1, 4, 6]

Edit: Using the same prompt as above:

-Incognito mode sessions get random:

[3, 10, 1, 7, 2, 8, 4, 9, 5, 6, 1, 4, 9, 2, 10, 3, 8, 7, 6, 5, 7, 3, 10, 8, 4, 2, 9, 1, 5, 6, 6, 8, 2, 9, 3, 10, 5, 7, 1, 4, 5, 9, 3, 7, 8, 2, 6, 10, 1, 4, 2, 7, 5, 9, 10, 8, 3, 4, 6, 1, 4, 1, 8, 10, 5, 9, 7, 6, 3, 2, 9, 5, 6, 2, 7, 10, 4, 3, 8, 1, 8, 4, 2, 9, 1, 6, 10, 5, 3, 7, 10, 6, 9, 3, 8, 5, 1, 7, 2, 4]

[8, 4, 2, 7, 10, 6, 1, 9, 5, 3, 2, 10, 6, 3, 8, 5, 9, 7, 4, 1, 7, 9, 5, 2, 6, 1, 10, 8, 3, 4, 4, 6, 10, 8, 7, 3, 9, 1, 2, 5, 3, 9, 8, 10, 2, 5, 6, 7, 1, 4, 6, 2, 7, 1, 8, 10, 9, 4, 3, 5, 9, 5, 4, 7, 10, 8, 3, 6, 2, 1, 1, 3, 8, 9, 2, 10, 4, 7, 6, 5, 10, 7, 9, 3, 4, 6, 8, 5, 2, 1, 5, 8, 6, 10, 9, 1, 7, 2, 4, 3]

-Normal browser sessions get loops:

3, 7, 1, 9, 5, 10, 4, 6, 2, 8, 1, 10, 3, 5, 7, 9, 2, 6, 8, 4, 9, 5, 3, 10, 1, 7, 6, 2, 8, 4, 5, 9, 10, 1, 3, 7, 4, 8, 6, 2, 9, 5, 10, 7, 1, 3, 8, 4, 6, 2, 5, 9, 10, 1, 7, 3, 4, 8, 6, 2, 5, 9, 10, 1, 3, 7, 4, 8, 2, 6, 5, 9, 10, 1, 3, 7, 4, 8, 6, 2, 5, 9, 10, 1, 7, 3, 8, 4, 6, 2, 5, 9, 10, 1, 7, 3, 4, 8, 6, 2

7, 3, 10, 2, 6, 9, 5, 1, 8, 4, 2, 10, 7, 5, 3, 6, 8, 1, 4, 9, 10, 7, 5, 2, 8, 4, 1, 6, 9, 3, 5, 10, 2, 7, 8, 1, 9, 4, 6, 3, 10, 7, 2, 5, 9, 8, 6, 4, 1, 3, 5, 9, 10, 8, 6, 2, 7, 4, 1, 3, 9, 5, 10, 7, 8, 6, 2, 4, 1, 3, 9, 5, 10, 7, 8, 2, 6, 4, 1, 9, 5, 10, 3, 7, 8, 6, 2, 4, 9, 1, 5, 10, 7, 3, 8, 6, 2, 4, 9, 1

This test was conducted with Android & Firefox 128, both Chatgpt sessions were not logged in, yet normal browsing holds a few instances of chatgpt.com visits.

Re: Poker Tournament for LLMs

#80

Post by RivieraKid »

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 are you working on specifically? I've been vaguely following poker research since Libratus, the last paper I've read is ReBeL, has there been any meaningful progress after that?

I was thinking about developing a 5-max poker agent that can play decently (not superhumanly), but it still seems like a kind of uncharted territory, there's Pluribus but limited to fixed stacks, very complex and very computationally demanding to train and I think also during gameplay.

I don't see why a LLM can't learn to play a mixed strategy. A LLM outputs a distribution over all tokens, which is then randomly sampled from.

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