Getting 50% (SoTA) on Arc-AGI with GPT-4o

(ARC Prize co-founder here).

Ryan's work is legitimately interesting and novel "LLM reasoning" research! The core idea:

> get GPT-4o to generate around 8,000 python programs which attempt to implement the transformation, select a program which is right on all the examples (usually there are 3 examples), and then submit the output this function produces when applied to the additional test input(s)

Roughly, he's implemented an outer loop and using 4o to sample reasoning traces/programs from training data and test. Hybrid DL + program synthesis approaches are solutions we'd love to see more of.

A couple important notes:

1. this result is on the public eval set vs private set (ARC Prize $).

2. the current private set SOTA ~35% solution also performed ~50% on the public set. so this new result might be SOTA but hasn't been validated or scrutinized yet.

All said, I do expect verified public set results to flow down to the private set over time. We'll be publishing all the SOTA scores and open source reproductions here once available: https://arcprize.org/leaderboard

EDIT: also, congrats and kudos to Ryan for achieving this and putting the effort in to document and share his approach. we hope to inspire more frontier AI research sharing like this

Maybe I am missing something, but to me this looks like "Let's brute-force on the training data".

I mean, generating tens of thousands of possible solutions, to find one that works does not, to me, signify AGI.

After all, the human solving these problem doesn't make 10k attempts before getting a solution, do they?

The approach here, due to brute force, can't really scale: if a random solution to a very simple problem has a 1/10k chance of being right, you can't scale this up to non-trivial problems without exponentially increasing the computational power used. Hence, I feel this is brute-force.

10000 samples are nothing compared to 2^100 possible outputs. It is absolutely, definitely not a "brute search". Testing a small fraction of possibilities (e.g. 0.000001%) is called heuristics, and that's what people use too.

Please learn a bit of combinatorics.

> After all, the human solving these problem doesn't make 10k attempts before getting a solution, do they?

No. People have much better "early rejection", also human brain has massive parallel compute capacity.

It's ridiculous to demand GPT-4 performs as good as a human. Obviously its vision is much worse and it doesn't have 'video' and physics priors people have, so it has to guess more times.

> 10000 samples are nothing compared to 2^100 possible outputs. It is absolutely, definitely not a "brute search". Testing a small fraction of possibilities (e.g. 0.000001%) is called heuristics, and that's what people use too.

Brute searching literally means generating solutions until one works. Which is exactly what is being done here.

> Please learn a bit of combinatorics.

Don't be condescending - I understand the problem space just fine. Fine enough to realise that the problem was constructed specifically to ensure that "solutions" such as this just won't work.

Which is why this "solution" is straight-up broken (doesn't meet the target, exceeds the computationally bounds, etc).

> It's ridiculous to demand GPT-4 performs as good as a human.

Wasn't the whole point of this prize to spur interest in a new approach to learning? What does GPT-[1234] have to do with the contest rules? Especially since this solution broke those rules anyway?

> Obviously its vision is much worse and it doesn't have 'video' and physics priors people have, so it has to guess more times.

That's precisely my point - it has to guess. Humans aren't guessing for those types of problems (not for the few that I saw anyway).

> I studied algorithms for years.

Who hasn't?

> You're 100% WRONG on everything you wrote.

Maybe you should update the wikipedia page, then all the other textbooks, that uses a definition of brute-force that matches my understanding of it.

From https://en.wikipedia.org/wiki/Brute-force_search

> Therefore, brute-force search is typically used when the problem size is limited, or when there are problem-specific heuristics that can be used to reduce the set of candidate solutions to a manageable size.

Further, in the same page https://en.wikipedia.org/wiki/Brute-force_search#Speeding_up...

> One way to speed up a brute-force algorithm is to reduce the search space, that is, the set of candidate solutions, by using heuristics specific to the problem class.

I mean, the approach under discussion is literally exactly this.

Now, Mr "ACM ICPC, studied algorithms for years", where's your reference that reducing the solution space using heuristics results in a non-brute-force algorithm?