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Grandmaster-Level Chess Without Search

arxiv.org

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Re: Grandmaster-Level Chess Without Search

#51

Post by YeGoblynQueenne »

paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing.

That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot.

The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of moves and a neural-net based system that generates moves similar to the ones in the dataset. DeepMind arbitrarily draw the boundary of the system around the neural net component and pretend that because the search is external to the neural net, the neural net doesn't need the search.

And yet, without the search there is no dataset, and without the dataset there is no model. They didn't train their system by self-play and they certainly didn't hire an army of low-paid workers to annotate moves for them. They generated training moves with a search-based system and learned to reproduce them. They used chickens to make eggs.

Their approach depends entirely on there being a powerful chess search engine and they wouldn't be able to create their system without it as a main component. Their "without search" claim is just a marketing term.

Re: Grandmaster-Level Chess Without Search

#52

Post by jedberg »

Kon5ole wrote: post #35
jedberg wrote: post #20

Now do Go. :)

This used to be a comforting thought whenever computers beat humans in chess, but I think that time has passed. The paper mentions AlphaZero [1], which has beaten AlphaGo, which beat Lee Sedol back in 2016 [2]. [1] https://en.wikipedia.org/wiki/AlphaZero [2] https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol

My pithy comment probably wasn't enough to express what I meant. :)

I know that computers have already beaten humans at Go. But what's interesting is that in both the chess and Go cases, a lot of real-time compute was necessary to win the games. Now we have a potential way to build the model ahead of time such that the computer during interactive play is much smaller.

This means that we can be much more portable with the solution, and it also means that for online game companies, they can spend a lot less money on gameplay, especially if gameplay is most of their compute.

Re: Grandmaster-Level Chess Without Search

#53

Post by jncfhnb »

IIAOPSW wrote: post #26

Slightly off topic but am I the only one that approaches strategy games by making a "zeroth order approximation". Eg find the shortest path to victory under the (obviously faulty) assumption that my opponent does nothing and the board is unchanging except for my moves. Now find my opponents shortest path to victory under the same assumption. Then evaluate, if we both just ignore each other and try to bum rush the vic…

Ehh idk. Sounds like it’s prone to the beginner strategy of assuming your opponent will occasionally do something really dumb.

Re: Grandmaster-Level Chess Without Search

#54

Post by famouswaffles »

janalsncm wrote: post #50
famouswaffles wrote: post #41

Earlier quoted context omitted.

>We don’t know if it’s using implicit search either. Sure >it’s also possible it has just memorized the evaluations from 10M games and is performing some function of the similarity of the input to those previously seen. That's not possible. The possible set of moves in chess is incredibly large and it is incredibly easy to play a game that has diverged from training. a model that has just memorized all evaluations wo…

If it could reliably win a mate in N position without inexplicably blundering, I would be more inclined to buy your search hypothesis. But it doesn’t, which is one of the reasons the authors gave for finishing with stockfish. So whatever it’s doing is clearly lossy which an actual search would not be. Neural nets memorize all sorts of things. They memorize ad clicks in high dimensional state spaces. Transformers trai…

>If it could reliably win a mate in N position without inexplicably blundering, I would be more inclined to buy your search hypothesis.

I don't have a "search hypothesis". I don't know what strategy the model employs to play. I was simply pointing out that limited search learned by the transformer is not out of the question. Stockfish finishing is not necessary to play chess well above the level a memorization hypothesis makes any sense. This is not the first LLM chess machine.

>Neural nets memorize all sorts of things. They memorize ad clicks in high dimensional state spaces. Transformers trained on the whole internet can often reproduce entire texts. It’s lossy, but it’s still memorizing.

Intelligent things memorize. Humans memorize a lot. I never said the model hasn't memorized a fair few things. Many human chess grandmaster memorize openings. What i'm saying is that it's not playing games via memorization any more than a human is doing the same.

>That seems like the simplest explanation for what’s happening here. There’s some sort of lossy memorization, not a search.

The options aren't only lossy memorization or lossless search.

Re: Grandmaster-Level Chess Without Search

#55

Post by penjelly »

i dont follow... even if its trained anc doesnt use search isnt the act of it deciding the next move a sortof search anyway based off its training? Ive heard people describe LLMs as extremely broad search, basically attempting to build world model and then predicting the next world based on that. Is this fundamentally different from search? Am i wrong in my assumptions here?

Re: Grandmaster-Level Chess Without Search

#56

Post by YeGoblynQueenne »

YeGoblynQueenne wrote: post #51
paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing. That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot. The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of mo…

Btw, just to be a bit more constructive (not by much) the proper term for what DeepMind did is "neuro-symbolic AI". But DeepMind shunned the term even for AlphaGO, a system comprised of a couple of neural nets and Monte-Carlo Tree Search.

The whole thing is just political: DeepMind use neural nets, GOFAI is dead and that's the way to AI. That's their story and they're sticking with it.

Re: Grandmaster-Level Chess Without Search

#57

Post by ogogmad »

YeGoblynQueenne wrote: post #51
paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing. That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot. The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of mo…

The point -- which I don't think you got -- is that extremely generic ingredients like high-quality data (which is the point of Stockfish here) and very deep Transformer-type Neural Networks, are enough to nearly match the performance of ad-hoc, non-generalisable techniques like gametree search algorithms.

This has two possible applications: 1. There's far less need to invent techniques like MCTS in the first place. 2. A single AI might be able to play grandmaster level chess by accident.

The catch is you need high quality data in large amounts.

Re: Grandmaster-Level Chess Without Search

#58

Post by YeGoblynQueenne »

ogogmad wrote: post #57
YeGoblynQueenne wrote: post #51

Earlier quoted context omitted.

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing. That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot. The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of mo…

The point -- which I don't think you got -- is that extremely generic ingredients like high-quality data (which is the point of Stockfish here) and very deep Transformer-type Neural Networks, are enough to nearly match the performance of ad-hoc, non-generalisable techniques like gametree search algorithms. This has two possible applications: 1. There's far less need to invent techniques like MCTS in the first place.…

I did get the point and I'm commenting that the point is missing the point. There is nothing new in learning that a large neural net can approximate the output of a classical system. This has been done many times before. The real point is that DeepMind build a system that is half-search and pretend it's no-search. You cannot get the "high-quality data" without a classical system- not in chess.

Re: Grandmaster-Level Chess Without Search

#59

Post by entropicdrifter »

jrockway wrote: post #43
dayjaby wrote: post #33

Earlier quoted context omitted.

On beginner level this might work, but if people are more competitive they begin to realize the benefit of not only playing the own game, but reading the enemies plan (e.g. scouting in Starcraft/AoE2) to counteract it as much as possible. Chess against humen is different. Usually, there is no path to victory, only to remis. People just follow strategic plans that people told them would be slightly beneficial later on…

I agree with this. My default approach to board games is basically to maximize victory points early. This usually works; when 4 people are playing a new game for the first time, I usually win. This doesn't really work when people know how to play the game specifically, though. I think this algorithm is better than many other algorithms that people come up with, however. (As an aside, when I play a card game I sort my…

This comment got me thinking about how I sort my cards. I scan the whole hand, then make the biggest changes first (e.g. consolidating suits/card types), then sort the subgroups.

Huh, guess I'm doing a sort of human heuristic version of Quicksort

Re: Grandmaster-Level Chess Without Search

#60

Post by [deleted] »

YeGoblynQueenne wrote: post #56
YeGoblynQueenne wrote: post #51

Earlier quoted context omitted.

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing. That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot. The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of mo…

Btw, just to be a bit more constructive (not by much) the proper term for what DeepMind did is "neuro-symbolic AI". But DeepMind shunned the term even for AlphaGO, a system comprised of a couple of neural nets and Monte-Carlo Tree Search. The whole thing is just political: DeepMind use neural nets, GOFAI is dead and that's the way to AI. That's their story and they're sticking with it.

[deleted]
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