[eclark]

Early game bluffs are essentially lies that you tell through the rest of the streets. In order to keep your opponents from knowing when you have premium starting hands, it's required to play some ranges, sometimes as if they were a different range. E.g., 10% of the time, I will bluff and act like I have AK, KK, AA, QQ. On the next street, I will need to continue that; otherwise, it becomes not profitable (opponents only need to wait one bet to know if I am bluffing). I have to evolve the lie as well. If cards come out that make my story more or less likely/profitable/possible, then I need to adjust the lie, not revert to the truth or the opponent's truth.

To see that LLMs aren't capable of this, I present all of the prompt jailbreaks that rely on repeated admonitions. And that makes sense if you think about the training data. There's not a lot of human writing that takes a fact and then confidently asserts the opposite as data mounts.

LLMs produce the most likely response from the input embeddings. Almost always, the easiest is that the next token is in agreement of the other tokens in the sequence. The problem in poker is that a good amount of the tokens in the sequence are masked and/or controlled by a villain who is actively trying to deceive.

Also, notice that I'm careful to say LLM's and not generalize to all attention head + MLP models. As attention with softmax and dot product is a good universal function. Instead, it's the large language model part that makes the models not great fits for poker. Human text doesn't have a latent space that's written about enough and thoroughly enough to have poker solved in there.

[eru]

I wouldn't call a bluff a lie. In the sense that you can tell anyone who asks honestly about your general policy around bluffing and that would not diminish how well your bluffs work. In contrast with lying, where you going around and saying "Oh, yeah, I tend to lie around 10% of the time." would backfire quite a bit.

In game theory, the point of bluffing is not so much to make money from your bluff directly, but to mask when you are playing a genuinely good hand.

> [...] it's required to play some ranges, sometimes as if they were a different range; [...]

Why the mental gymnastics? Just say what the optimal play for 'some ranges' is, and then play that. The extra indirection in explanation might be useful for human intuition, but I'm not sure the machine needs that dressing up.

> LLMs produce the most likely response from the input embeddings. [...]

If I wanted to have my LLM play poker, I would ask it suggest me probabilities for what to play next, and then sample from there, instead of using the next-token sampler in the LLM to directly tell you the action you should take.

(But I'm not sure that's what the original article is doing.)

> The problem in poker is that a good amount of the tokens in the sequence are masked and/or controlled by a villain who is actively trying to deceive.

> Human text doesn't have a latent space that's written about enough and thoroughly enough to have poker solved in there.

I agree with both. Though it's still a fun exercise to pit contemporary off-the-shelf LLMs against each other here.

And perhaps add a purpose built poker bot to the mix as a benchmark. And also try with and without access to an external random sampler (like I suggested above). Or with and without access to eg being able to run freshly written Python code.