4 ms·
Are there any single-step non-reasoner models that do well on this benchmark? I wonder how well the latest Claude 3.5 Sonnet does on this benchmark and if it's
by dtquad 2y ago
Are there any single-step non-reasoner models that do well on this benchmark?
I wonder how well the latest Claude 3.5 Sonnet does on this benchmark and if it's near o1.
- throwaway71271 2y ago| Name | Semi-private eval | Public eval | |--------------------------------------|-------------------|-------------| | Jeremy Berman | 53.6% | 58.5% | | Akyürek et al. | 47.5% | 62.8% | | Ryan Greenblatt | 43% | 42% | | OpenAI o1-preview (pass@1) | 18% | 21% | | Anthropic Claude 3.5 Sonnet (pass@1) | 14% | 21% | | OpenAI GPT-4o (pass@1) | 5% | 9% | | Google Gemini 1.5 (pass@1) | 4.5% | 8% | https://arxiv.org/pdf/2412.04604 https://arxiv.org/pdf/2412.04604
- kandesbunzler 2y agowhy is this missing the o1 release / o1 pro models? Would love to know how much better they are
- Freebytes 2y agoThis might be because they are referencing single step, and I do not think o1 is single step.
- aimanbenbaha 2y agoAkyürek et al uses test-time compute.
- YetAnotherNick 2y agoHere are the results for base models[1]: o3 (coming soon) 75.7% 82.8% o1-preview 18% 21% Claude 3.5 Sonnet 14% 21% GPT-4o 5% 9% Gemini 1.5 4.5% 8% Score (semi-private eval) / Score (public eval) [1]: https://arcprize.org/2024-results https://arcprize.org/2024-results
- simonw 2y agoI'd love to know how Claude 3.5 Sonnet does so well despite (presumably) not having the same tricks as the o-series models.
- Bjorkbat 2y agoIt's easy to miss, but if you look closely at the first sentence of the announcement they mention that they used a version of o3 trained on a public dataset of ARC-AGI, so technically it doesn't belong on this list.
- dot1x 2y agoIt's all scam. ClosedAI trained on the data they were tested on, so no, nothing here is impressive.
- Tanjreeve 2y agoThey definitely did or they probably did? Is there any source for that just so I can point It out to people?
- catalypso 2y agoJust a clarification, they tuned on the public training dataset, not the semi-private one. The 87.5% score was on the semi-private eval, which means the model was still able to generalize well. That being said, the fact that this is not a "raw" base model, but one tuned on the ARC-AGI tests distribution takes away from the impressiveness of the result — How much ? — I'm not sure, we'd need the un-tuned base o3 model score for that. In the meantime, comparing this tuned o3 model to other un-tuned base models is unfair (apples-to-oranges kind of comparison).