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Poker Tournament for LLMs

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181–190 of 212 posts

Re: Poker Tournament for LLMs

#181
post #98

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 Would a LLM with tool calls be able to do this?

Yes, ChatGPT can do it using Python today (the statsmodels library). I use it all the time (I’m a statistician).

Re: Poker Tournament for LLMs

#183
post #5

I wonder if these will get better over time. Fun idea and I kind of want to join a table. For now at least, some can't even determine which hand they have: > LLAMA bets $170 on Flop > "We have top pair with Tc4d on a flop of 2s Ts Jh. The board is relatively dry, and we have a decent chance of having the best hand. We're betting $170.00 to build the pot and protect our hand." (That's not top pair)

and the board isn't dry (there are straight and flush draws).

Re: Poker Tournament for LLMs

#185
post #118

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.

Set the temperature to zero and that's exactly what you get.

In some NN implementations, randomness is actually pretty important to keep the gradients from getting stuck at local minima/maxima. Is that true for LLMs, or is it not something that applies at all?

Re: Poker Tournament for LLMs

#186
post #124
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.

>They would need to lie, which they can't currently do

They lie better than most people lol.

Re: Poker Tournament for LLMs

#187

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…

At least Random numbers problem can easily be solved by giving LLM access to the corresponding tool.

Re: Poker Tournament for LLMs

#188

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…

I decided to try this: > sample a random number from 1 to 10 > ChatGPT: Here’s a random number between 1 and 10: 7 > again > ChatGPT: Your random number is: 3

that's pretty funny.

> give me 11 random numbers in a set with range 1-10, allowing duplicates

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

I repeated it three times, 3 and 7 were always the first two elements haha.

(I get why, and get why this is stupid to expect it to do, but it still gave me a laugh.)

Re: Poker Tournament for LLMs

#189
post #188

Earlier quoted context omitted.

I decided to try this: > sample a random number from 1 to 10 > ChatGPT: Here’s a random number between 1 and 10: 7 > again > ChatGPT: Your random number is: 3

that's pretty funny. > give me 11 random numbers in a set with range 1-10, allowing duplicates > ChatGPT: [3, 7, 1, 4, 9, 2, 6, 3, 10, 8, 5] I repeated it three times, 3 and 7 were always the first two elements haha. (I get why, and get why this is stupid to expect it to do, but it still gave me a laugh.)

in case my comment made someone wonder what the 'right'* way to do this is, if you needed to for some reason.

> give me 11 random numbers in a set with range 1-10, allowing duplicates. if you don't think an LLM can generate properly pseudorandom numbers, then use your tools to generate them.

This caused it to create and execute a python script that returned

  [random.randint(1, 10) for _ in range(11)]
which, of course, worked.

* obviously don't leave it up to the model to decide about whether it can do random numbers. I just wanted to see what it would do..

Re: Poker Tournament for LLMs

#190

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…

why can't it just use tool calling for RNG?
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