3 ms·

LLMs are capable of playing chess and 3.5 turbo instruct does so quite well (for a human) at 1800 ELO. Does this mean they can truly reason now ? https://githu

LLMs are capable of playing chess and 3.5 turbo instruct does so quite well (for a human) at 1800 ELO. Does this mean they can truly reason now ? https://github.com/adamkarvonen/chess_gpt_eval https://github.com/adamkarvonen/chess_gpt_eval

hatefulmoron 2y ago
3.5 turbo instruct is a huge outlier. https://dynomight.substack.com/p/chess https://dynomight.substack.com/p/chess Discussion here: https://news.ycombinator.com/item?id=42138289 https://news.ycombinator.com/item?id=42138289
- famouswaffles 2y ago
  That might be overstating it, at least if you mean it to be some unreplicable feat. Small models have been trained that play around 1200 to 1300 on the eleuther discord. And there's this grandmaster level transformer - https://arxiv.org/html/2402.04494v1 https://arxiv.org/html/2402.04494v1 Open AI, Anthropic and the like simply don't care much about their LLMs playing chess. That or post training is messing things up.
  - hatefulmoron 2y ago
    > That might be overstating it, at least if you mean it to be some unreplicable feat. I mean, surely there's a reason you decided to mention 3.5 turbo instruct and not.. 3.5 turbo? Or any other model? Even the ones that came after? It's clearly a big outlier, at least when you consider "LLMs" to be a wide selection of recent models. If you're saying that LLMs/transformer models are capable of being trained to play chess by training on chess data, I agree with you. I think AstroBen was pointing out that LLMs, despite having the ability to solve some very impressive mathematics and programming tasks, don't seem to generalize their reasoning abilities to a domain like chess. That's surprising, isn't it?
    - famouswaffles 2y ago
      I mentioned it because it's the best example. One example is enough to disprove the "not capable of". There are other examples too. >I think AstroBen was pointing out that LLMs, despite having the ability to solve some very impressive mathematics and programming tasks, don't seem to generalize their reasoning abilities to a domain like chess. That's surprising, isn't it? Not really. The LLMs play chess like they have no clue what the rules of the game are, not like poor reasoners. Trying to predict and failing is how they learn anything. If you want them to learn a game like chess then how you get them to learn it - by trying to predict chess moves. Chess books during training only teach them how to converse about chess.
      - hatefulmoron 2y ago
        > One example is enough to disprove the "not capable of" nonsense. There are other examples too. Gotcha, fair enough. Throw enough chess data in during training, I'm sure they'd be pretty good at chess. I don't really understand what you're trying to say in your next paragraph. LLMs surely have plenty of training data to be familiar with the rules of chess. They also purportedly have the reasoning skills to use their familiarity to connect the dots and actually play. It's trivially true that this issue can be plastered over by shoving lots of chess game training data into them, but the success of that route is not a positive reflection on their reasoning abilities.
        famouswaffles 2y ago
        Gradient descent is a dumb optimizer. LLM training is not at all like a human reading a book and more like evolution tuning adaptations over centuries. You would not expect either process to be aware of anything they are converging towards. So having lots of books that talk about chess in training will predictably just return a model that knows how to talk about chess really well. I'm not surprised they may know how to talk about the rules but play them poorly. And that post had a follow-up. Post-training messing things up could well be the issue seeing the impact even a little more examples and/or regurgitation made. https://dynomight.net/more-chess/ https://dynomight.net/more-chess/
AstroBen 2y ago
My point wasn't chess specific or that they couldn't have specific training for it. It was a more general "here is something that LLMs clearly aren't being trained for currently, but would also be solvable through reasoning skills" Much in the same way a human who only just learnt the rules but 0 strategy would very, very rarely lose here These companies are shouting that their products are passing incredibly hard exams, solving PHD level questions, and are about to displace humans, and yet they still fail to crush a random-only strategy chess bot? How does this make any sense? We're on the verge of AGI but there's not even the tiniest spark of general reasoning ability in something they haven't been trained for "Reasoning" or "Thinking" are marketing terms and nothing more. If an LLM is trained for chess then its performance would just come from memorization, not any kind of "reasoning"
- famouswaffles 2y ago
  >If an LLM is trained for chess then its performance would just come from memorization, not any kind of "reasoning". If you think you can play chess at that level over that many games and moves with memorization then i don't know what to tell you except that you're wrong. It's not possible so let's just get that out of the way. >These companies are shouting that their products are passing incredibly hard exams, solving PHD level questions, and are about to displace humans, and yet they still fail to crush a random-only strategy chess bot? How does this make any sense? Why doesn't it ? Have you actually looked at any of these games ? Those LLMs aren't playing like poor reasoners. They're playing like machines who have no clue what the rules of the game are. LLMs learn by predicting and failing and getting a little better at it, repeat ad nauseum. You want them to learn the rules of a complex game ? That's how you do it. By training them to predict it. Training on chess books just makes them learn how to converse about chess. Humans have weird failure modes that are odds with their 'intelligence'. We just choose to call them funny names and laugh about it sometimes. These Machines have theirs. That's all there is to it. The top comment we are both replying to had gemini-2.5-pro which released less than 5 days later hit 25% on the benchmark. Now that was particularly funny.
  - AstroBen 2y ago
    > Why doesn't it? It was surprising to me because I would have expected if there was reasoning ability then it would translate across domains at least somewhat, but yeah what you say makes sense. I'm thinking of it in human terms

hatefulmoron 2y ago

3.5 turbo instruct is a huge outlier. https://dynomight.substack.com/p/chess https://dynomight.substack.com/p/chess Discussion here: https://news.ycombinator.com/item?id=42138289 https://news.ycombinator.com/item?id=42138289

famouswaffles 2y ago
That might be overstating it, at least if you mean it to be some unreplicable feat. Small models have been trained that play around 1200 to 1300 on the eleuther discord. And there's this grandmaster level transformer - https://arxiv.org/html/2402.04494v1 https://arxiv.org/html/2402.04494v1 Open AI, Anthropic and the like simply don't care much about their LLMs playing chess. That or post training is messing things up.
- hatefulmoron 2y ago
  > That might be overstating it, at least if you mean it to be some unreplicable feat. I mean, surely there's a reason you decided to mention 3.5 turbo instruct and not.. 3.5 turbo? Or any other model? Even the ones that came after? It's clearly a big outlier, at least when you consider "LLMs" to be a wide selection of recent models. If you're saying that LLMs/transformer models are capable of being trained to play chess by training on chess data, I agree with you. I think AstroBen was pointing out that LLMs, despite having the ability to solve some very impressive mathematics and programming tasks, don't seem to generalize their reasoning abilities to a domain like chess. That's surprising, isn't it?
  - famouswaffles 2y ago
    I mentioned it because it's the best example. One example is enough to disprove the "not capable of". There are other examples too. >I think AstroBen was pointing out that LLMs, despite having the ability to solve some very impressive mathematics and programming tasks, don't seem to generalize their reasoning abilities to a domain like chess. That's surprising, isn't it? Not really. The LLMs play chess like they have no clue what the rules of the game are, not like poor reasoners. Trying to predict and failing is how they learn anything. If you want them to learn a game like chess then how you get them to learn it - by trying to predict chess moves. Chess books during training only teach them how to converse about chess.
    - hatefulmoron 2y ago
      > One example is enough to disprove the "not capable of" nonsense. There are other examples too. Gotcha, fair enough. Throw enough chess data in during training, I'm sure they'd be pretty good at chess. I don't really understand what you're trying to say in your next paragraph. LLMs surely have plenty of training data to be familiar with the rules of chess. They also purportedly have the reasoning skills to use their familiarity to connect the dots and actually play. It's trivially true that this issue can be plastered over by shoving lots of chess game training data into them, but the success of that route is not a positive reflection on their reasoning abilities.
      - famouswaffles 2y ago
        Gradient descent is a dumb optimizer. LLM training is not at all like a human reading a book and more like evolution tuning adaptations over centuries. You would not expect either process to be aware of anything they are converging towards. So having lots of books that talk about chess in training will predictably just return a model that knows how to talk about chess really well. I'm not surprised they may know how to talk about the rules but play them poorly. And that post had a follow-up. Post-training messing things up could well be the issue seeing the impact even a little more examples and/or regurgitation made. https://dynomight.net/more-chess/ https://dynomight.net/more-chess/

AstroBen 2y ago

My point wasn't chess specific or that they couldn't have specific training for it. It was a more general "here is something that LLMs clearly aren't being trained for currently, but would also be solvable through reasoning skills" Much in the same way a human who only just learnt the rules but 0 strategy would very, very rarely lose here These companies are shouting that their products are passing incredibly hard exams, solving PHD level questions, and are about to displace humans, and yet they still fail to crush a random-only strategy chess bot? How does this make any sense? We're on the verge of AGI but there's not even the tiniest spark of general reasoning ability in something they haven't been trained for "Reasoning" or "Thinking" are marketing terms and nothing more. If an LLM is trained for chess then its performance would just come from memorization, not any kind of "reasoning"

famouswaffles 2y ago
>If an LLM is trained for chess then its performance would just come from memorization, not any kind of "reasoning". If you think you can play chess at that level over that many games and moves with memorization then i don't know what to tell you except that you're wrong. It's not possible so let's just get that out of the way. >These companies are shouting that their products are passing incredibly hard exams, solving PHD level questions, and are about to displace humans, and yet they still fail to crush a random-only strategy chess bot? How does this make any sense? Why doesn't it ? Have you actually looked at any of these games ? Those LLMs aren't playing like poor reasoners. They're playing like machines who have no clue what the rules of the game are. LLMs learn by predicting and failing and getting a little better at it, repeat ad nauseum. You want them to learn the rules of a complex game ? That's how you do it. By training them to predict it. Training on chess books just makes them learn how to converse about chess. Humans have weird failure modes that are odds with their 'intelligence'. We just choose to call them funny names and laugh about it sometimes. These Machines have theirs. That's all there is to it. The top comment we are both replying to had gemini-2.5-pro which released less than 5 days later hit 25% on the benchmark. Now that was particularly funny.
- AstroBen 2y ago
  > Why doesn't it? It was surprising to me because I would have expected if there was reasoning ability then it would translate across domains at least somewhat, but yeah what you say makes sense. I'm thinking of it in human terms