Comment by CamperBob2
3 days ago
They could still have trained the model in such a way as to focus on benchmarks, e.g. training on more examples of ARC style questions
That's kind of the idea behind ARC-AGI. Training on available ARC benchmarks does not generalize. Unless it does... in which case, mission accomplished.
Seems still possible to spend effort of building up an ARC-style dataset and that would game the test. The ARC questions I saw were not of some completely unknown topic, they were generally hard versions of existing problems in well-known domains. Not super familiar with this area in general though so would be curious if I'm wrong.
ARC-AGI isn't question- or knowledge-based, though, but "Infer the pattern and apply it to a new example you haven't seen before." The problems are meant to be easy for humans but hard for ML models, like a next-level CAPTCHA.
They have walked back the initial notion that success on the test requires, or demonstrates, the emergence of AGI. But the general idea remains, which is that no amount of pretraining on the publicly-available problems will help solve the specific problems in the (theoretically-undisclosed) test set unless the model is exhibiting genuine human-like intelligence.
Getting almost 16% on ARC-AGI-2 is pretty interesting. I wish somebody else had done it, though.
I’ve seen some of the problems before, like https://o3-failed-arc-agi.vercel.app/
This is not hard to build datasets that have these types of problems in them, and I would expect LLMs to generalize this well. I don’t see how this is any different really than any other type of problem LLMs are good at given they have the dataset to study.
I get they keep the test updated with secret problems, but I don’t see how companies can’t game this just by investing in building their own datasets, even if it means paying teams of smart people to generate them.
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