I agree. I think the Kaggle models have more advantages than o3. I think they have far more human design and fine-tuning than o3. One can almost argue that some Kaggle models are very slightly trained on the test set, in the sense the humans making them learn from test sets results, and empirically discover what improves such results.
o3′s defeating the Kaggle models is very impressive, but o3′s results shouldn’t be directly compared against other untuned models.
One can almost argue that some Kaggle models are very slightly trained on the test set
I’d say they’re more-than-trained on the test set. My understanding is that humans were essentially able to do an architecture search, picking the best architecture for handling the test set, and then also put in whatever detailed heuristics they wanted into it based on studying the test set (including by doing automated heuristics search using SGD, it’s all fair game). So they’re not “very slightly” trained, they’re trained^2.
Arguably the same is the case for o3, of course. ML researchers are using benchmarks as targets, and while they may not be directly trying to Goodhart to them, there’s still a search process over architectures-plus-training-loops whose termination condition is “the model beats a new benchmark”. And SGD itself is, in some ways, a much better programmer than any human.
So o3′s development and training process essentially contained the development-and-training process for Kaggle models. They’ve iteratively searched for an architecture that can be trained to beat several benchmarks, then did so.
They did this on the far easier training set though?
An alternative story is they trained until a model was found that could beat the training set but many other benchmarks too, implying that there may be some general intelligence factor there. Maybe this is still goodharting on benchmarks but there’s probably truly something there.
There are degrees of Goodharting. It’s not Goodharting to ARC-AGI specifically, but it is optimizing for performance on the array of easily-checkable benchmarks. Which plausibly have some common factor between them to which you could “Goodhart”; i. e., a way to get good at them without actually training generality.
I agree. I think the Kaggle models have more advantages than o3. I think they have far more human design and fine-tuning than o3. One can almost argue that some Kaggle models are very slightly trained on the test set, in the sense the humans making them learn from test sets results, and empirically discover what improves such results.
o3′s defeating the Kaggle models is very impressive, but o3′s results shouldn’t be directly compared against other untuned models.
I’d say they’re more-than-trained on the test set. My understanding is that humans were essentially able to do an architecture search, picking the best architecture for handling the test set, and then also put in whatever detailed heuristics they wanted into it based on studying the test set (including by doing automated heuristics search using SGD, it’s all fair game). So they’re not “very slightly” trained, they’re trained^2.
Arguably the same is the case for o3, of course. ML researchers are using benchmarks as targets, and while they may not be directly trying to Goodhart to them, there’s still a search process over architectures-plus-training-loops whose termination condition is “the model beats a new benchmark”. And SGD itself is, in some ways, a much better programmer than any human.
So o3′s development and training process essentially contained the development-and-training process for Kaggle models. They’ve iteratively searched for an architecture that can be trained to beat several benchmarks, then did so.
They did this on the far easier training set though?
An alternative story is they trained until a model was found that could beat the training set but many other benchmarks too, implying that there may be some general intelligence factor there. Maybe this is still goodharting on benchmarks but there’s probably truly something there.
There are degrees of Goodharting. It’s not Goodharting to ARC-AGI specifically, but it is optimizing for performance on the array of easily-checkable benchmarks. Which plausibly have some common factor between them to which you could “Goodhart”; i. e., a way to get good at them without actually training generality.