Recent AI model progress feels mostly like bullshit

https://github.com/adamkarvonen/chess_gpt_eval

https://dynomight.substack.com/p/chess

Discussion here: https://news.ycombinator.com/item?id=42138289

Open AI, Anthropic and the like simply don't care much about their LLMs playing chess. That or post training is messing things up.

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"

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 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.

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.

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.

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/

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

Like how

- Training LLMs on code makes them solve reasoning problems better - Training Language Y alongside X makes them much better at Y than if they were trained on language Y alone and so on.

Probably because well gradient descent is a dumb optimizer and training is more like evolution than a human reading a book.

Also, there is something genuinely weird going on with LLM chess. And it's possible base models are better. https://dynomight.net/more-chess/

This whole premise crashes and burns if you need task-specific training, like explicit chess training. That is because there are far too many tasks that humans need to be competent at in order to be useful in society. Even worse, the vast majority of those tasks are very hard to source training data for, unlike chess.

So, if we accept that LLMs can't learn chess unless they explicitly include chess games in the training set, then we have to accept that they can't learn, say, to sell business software unless they include business software pitches in the training set, and there are going to be FAR fewer of those than chess games.

And they do, just not always in the ways we expect.

>This whole premise crashes and burns if you need task-specific training, like explicit chess training.

Everyone needs task specific training. Any human good at chess or anything enough to make it a profession needs it. So I have no idea why people would expect any less for a Machine.

>then we have to accept that they can't learn, say, to sell business software unless they include business software pitches in the training set, and there are going to be FAR fewer of those than chess games.

Yeah so ? How much business pitches they need in the training set has no correlation with chess. I don't see any reason to believe what is already present isn't enough. There's enough chess data on the internet to teach them chess too, it's just a matter of how much open AI care about it.

Very hard for me to wrap my head around the idea that an LLM being able to discuss, even perhaps teach high level chess strategy wouldn't transfer at all to its playing performance

So, the fact that LLMs can't learn this sample game despite probably including all of the books ever written on it in their training set tells us something about their general reasoning skills.

Yes, that's all there is to it and it's not enough. I ain't paying for another defective organism that makes mistakes in entirely novel ways. At least with humans you know how to guide them back on course.

If that's the peak of "AI" evolution today, I am not impressed.