Comment by grugagag
1 year ago
LLMs like GPT aren’t built to play chess, and here’s why: they’re made for handling language, not playing games with strict rules and strategies. Chess engines, like Stockfish, are designed specifically for analyzing board positions and making the best moves, but LLMs don’t even "see" the board. They’re just guessing moves based on text patterns, without understanding the game itself.
Plus, LLMs have limited memory, so they struggle to remember previous moves in a long game. It’s like trying to play blindfolded! They’re great at explaining chess concepts or moves but not actually competing in a match.
> but LLMs don’t even "see" the board
This is a very vague claim, but they can reconstruct the board from the list of moves, which I would say proves this wrong.
> LLMs have limited memory
For the recent models this is not a problem for the chess example. You can feed whole books into them if you want to.
> so they struggle to remember previous moves
Chess is stateless with perfect information. Unless you're going for mind games, you don't need to remember previous moves.
> They’re great at explaining chess concepts or moves but not actually competing in a match.
What's the difference between a great explanation of a move and explaining every possible move then selecting the best one?
Chess is not stateless. En Passant requires last move and castling rights requires nearly all previous moves.
https://adamkarvonen.github.io/machine_learning/2024/01/03/c...
Ok, I did go too far. But castling doesn't require all previous moves - only one bit of information carried over. So in practice that's board + 2 bits per player. (or 1 bit and 2 moves if you want to include a draw)
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Chess is not stateless. Three repetitions of same position is a draw.
Yes, there’s state there that’s not in the board position, but technically, threefold repetition is not a draw. Play can go on. https://en.wikipedia.org/wiki/Threefold_repetition:
“The game is not automatically drawn if a position occurs for the third time – one of the players, on their turn, must claim the draw with the arbiter. The claim must be made either before making the move which will produce the third repetition, or after the opponent has made a move producing a third repetition. By contrast, the fivefold repetition rule requires the arbiter to intervene and declare the game drawn if the same position occurs five times, needing no claim by the players.”
> Chess is stateless with perfect information. Unless you're going for mind games, you don't need to remember previous moves.
while it can be played as stateless, remembering previous moves gives you insight into potential strategy that is being build.
> Chess is stateless with perfect information.
It is not stateless, because good chess isn't played as a series of independent moves -- it's played as a series of moves connected to a player's strategy.
> What's the difference between a great explanation of a move and explaining every possible move then selecting the best one?
Continuing from the above, "best" in the latter sense involves understanding possible future moves after the next move.
Ergo, if I looked at all games with the current board state and chose the next move that won the most games, it'd be tactically sound but strategically ignorant.
Because many of those next moves were making that next move in support of some broader strategy.
> it's played as a series of moves connected to a player's strategy.
That state belongs to the player, not to the game. You can carry your own state in any game you want - for example remember who starts with what move in rock paper scissors, but that doesn't make that game stateful. It's the player's decision (or bot's implementation) to use any extra state or not.
I wrote "previous moves" specifically (and the extra bits already addressed elsewhere), but the LLM can carry/rebuild its internal state between the steps.
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> good chess isn't played as a series of independent moves -- it's played as a series of moves connected to a player's strategy.
Maybe good chess, but not perfect chess. That would by definition be game-theoretically optimal, which in turn implies having to maintain no state other than your position in a large but precomputable game tree.
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You can feed them whole books, but they have trouble with recall for specific information in the middle of the context window.
>Chess is stateless with perfect information. Unless you're going for mind games, you don't need to remember previous moves.
In what sense is chess stateless? Question: is Rxa6 a legal move? You need board state to refer to in order to decide.
They mean that you only need board position, you don't need the previous moves that led to that board position.
There are at least a couple of exceptions to that as far as I know.
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> they’re made for handling language, not playing games with strict rules and strategies
Here's the opposite theory: Language encodes objective reasoning (or at least, it does some of the time). A sufficiently large ANN trained on sufficiently large amounts of text will develop internal mechanisms of reasoning that can be applied to domains outside of language.
Based on what we are currently seeing LLMs do, I'm becoming more and more convinced that this is the correct picture.
I share this idea but from the different perspective. It doesn’t develop these mechanisms, but casts a high-dimensional-enough shadow of their effect on itself. This vaguely explains why the more deep Gell-Mann-wise you are the less sharp that shadow is, because specificity cuts off “reasoning” hyperplanes.
It’s hard to explain emerging mechanisms because of the nature of generation, which is one-pass sequential matrix reduction. I say this while waving my hands, but listen. Reasoning is similar to Turing complete algorithms, and what LLMs can become through training is similar to limited pushdown automata at best. I think this is a good conceptual handle for it.
“Line of thought” is an interesting way to loop the process back, but it doesn’t show that much improvement, afaiu, and still is finite.
Otoh, a chess player takes as much time and “loops” as they need to get the result (ignoring competitive time limits).
LLMs need to compress information to be able to predict next words in as many contexts as possible.
Chess moves are simply tokens as any other. Given enough chess training data, it would make sense to have part of the network trained to handle chess specifically instead of simply encoding basic lists of moves and follow-ups. The result would be a general purpose sub-network trained on chess.
Language is a game with strict rules and strategies.
just curious, was this rephrased by an llm or is that your writing style?