Comment by usaar333
9 months ago
And then within a week, Gemini 2.5 was tested and got 25%. Point is AI is getting stronger.
And this only suggested LLMs aren't trained well to write formal math proofs, which is true.
9 months ago
And then within a week, Gemini 2.5 was tested and got 25%. Point is AI is getting stronger.
And this only suggested LLMs aren't trained well to write formal math proofs, which is true.
> within a week
How do we know that Gemini 2.5 wasn't specifically trained or fine-tuned with the new questions? I don't buy that a new model could suddenly score 5 times better than the previous state-of-the-art models.
They retrained their model less than a week before its release, just to juice one particular nonstandard eval? Seems implausible. Models get 5x better at things all the time. Challenges like the Winograd schema have gone from impossible to laughably easy practically overnight. Ditto for "Rs in strawberry," ferrying animals across a river, overflowing wine glass, ...
The "ferrying animals across a river" problem has definitely not been solved, they still don't understand the problem at all, overcomplicating it because they're using an off-the-shelf solution instead of actual reasoning:
o1 screwing up a trivially easy variation: https://xcancel.com/colin_fraser/status/1864787124320387202
Claude 3.7, utterly incoherent: https://xcancel.com/colin_fraser/status/1898158943962271876
DeepSeek: https://xcancel.com/colin_fraser/status/1882510886163943443#...
Overflowing wine glass also isn't meaningfully solved! I understand it is sort of solved for wine glasses (even though it looks terrible and unphysical, always seems to have weird fizz). But asking GPT to "generate an image of a transparent vase with flowers which has been overfilled with water, so that water is spilling over" had the exact same problem as the old wine glasses: the vase was clearly half-full, yet water was mysteriously trickling over the sides. Presumably OpenAI RLHFed wine glasses since it was a well-known failure, but (as always) this is just whack-a-mole, it does not generalize into understanding the physical principle.
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Imagine that you are making problem solving AI. You have large budget, and access to compute and web crawling infra to run your AI "on internet". You would like to be aware of the ways people are currently evaluating AI so that you can be sure your product looks good. Do you have maybe an idea how one could do that?
>one particular nonstandard eval
A particular nonstandard eval that is currently top comment on this HN thread, due to the fact that, unlike every other eval out there, LLMs score badly on it?
Doesn't seem implausible to me at all. If I was running that team, I would be "Drop what you're doing, boys and girls, and optimise the hell out of this test! This is our differentiator!"
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I'm not generally inclined toward the "they are cheating cheaters" mindset, but I'll point out that fine tuning is not the same as retraining. It can be done cheaply and quickly.
Models getting 5X better at things all the time is at least as easy to interpret as evidence of task-specific tuning than as breakthroughs in general ability, especially when the 'things being improved on' are published evals with history.
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They could have rlhfed or finetuned on user thumbs up responses, which could include users who took the test and asked it to explain problems after
New models suddenly doing much better isn't really surprising, especially for this sort of test: going from 98% accuracy to 99% accuracy can easily be the difference between having 1 fatal reasoning error and having 0 fatal reasoning errors on a problem with 50 reasoning steps, and a proof with 0 fatal reasoning errors gets ~full credit whereas a proof with 1 fatal reasoning error gets ~no credit.
And to be clear, that's pretty much all this was: there's six problems, it got almost-full credit on one and half credit on another and bombed the rest, whereas all the other models bombed all the problems.
They are trained on some mix with minimal fraction of math. That's how it was from the beginning. But we can rebalance it by adding quality generated content. Just content will cost millions of $$ to generate. Distillation on new level looks like logical next step.