Comment by hatefulmoron
9 months 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?
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.
> 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.
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/
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The issue isn’t whether they can be trained to play. The issue is whether, after making a careful reading of the rules, they can infer how to play. The latter is something a human child could do, but it is completely beyond an LLM.
Reasoning training causes some about of catastrophic forgetting, so unlikely they burn that on mixing in chess puzzles if they want a commercial product, unless it somehow transfers well to other reasoning problems broadly cared about.