From ChatGPT 3.5 to o1, all LLMs progress came from investment in training: either by using much more data, or using higher quality data thanks to artificial data.
o1 (and then o3) broke this paradigm by applying a novel idea (RL+search on CoT) and that's because of it that it was able to make progress on ARC-AGI.
So IMO the success of o3 goes in favor of the argument of how we are in an idea-constrained environment.
This isn't a novel idea - some people tried the exact same thing the day GPT4 came out.
And going back even further, there's Goal Oriented Action Planning - an old timey video game AI technique, that's basically searching through solution space to construct a plan:
Not Greg/team, so unrelated opinion. o3 solution for ARC v1 was incredibly expensive. Some good ideas are at least needed to take that cost down by a factor 100-10000x.
Yeah my analogy for that solution is like claiming to have solved sorting arrays by using enormous compute to try all possible orderings of arrays of length 100.
From ChatGPT 3.5 to o1, all LLMs progress came from investment in training: either by using much more data, or using higher quality data thanks to artificial data.
o1 (and then o3) broke this paradigm by applying a novel idea (RL+search on CoT) and that's because of it that it was able to make progress on ARC-AGI.
So IMO the success of o3 goes in favor of the argument of how we are in an idea-constrained environment.