I feel like your take is overly cynical. The fact that humans can do the same thing by hand is not really the point. The contribution lies in the fact that their method derived this improvement *automatically*, which is where the impact lies. No one cares all that much if a human can make a sorting routine 2% faster, but if a program can do it, it suddenly becomes interesting (since it suggests that a similar approach can be applied to many other routines).
I am not cynical about the research itself, I am critical of claims such as "new sorting algorithm uncovered", "up to 70% faster", or "first change in a decade". The research is good. The achieved results are massively inflated.
What they achieved: automatically generated good code.
What they claim: automatically generated code that is revolutionary and an improvement on the state of the art.
There's also automatic searching being done on faster sorting networks that actually recently produced better than state of the art sorting networks: https://github.com/bertdobbelaere/SorterHunter
While I agree that the claims are hyperbolic, I think you are approaching this paper from the point of view of someone who knows a lot about sorting. Because of this, its normal that the claims of these guys who probably don't know much about it are grating for you.
But, at its core, this is really a RL paper. The objective is to see how far a generic approach can work while understanding as little as possible about the actual domain. After AlphaGo exceeded expectations, the question becomes: "What else can RL do, and can it do anything actually useful?", and this paper seems to suggest that it can optimize code pretty well! I'm really not sure they are self-aggrandizing in terms of impact. The impact of an approach like this could potentially be very large (although I'm not saying that it actually is, I don't know enough).
Sorting is not a niche topic. Anybody who majored in CS (which is a ton of people these days) will read the abstract and most of the paper thinking "Wow, I can't believe they discovered something better than O(N log N)" because that's usually what people mean when they say "better sorting algorithm". What they discovered here is effectively a new compiler optimization. They should present it as such instead of calling it a new sorting algorithm.
But ya, discovering a new compiler optimization automatically is kinda cool.
I hope people majoring in CS will not think that, as they learned that n log n is the theorically best complexity for a sort algorithm. They will rather think that they found an algorithm with better constants in front of n log n.
Anyone who does any basic algorithmic CS stuff would have been exposed to sorting algorithms, their variations, sorting networks and so on.
There are already superoptimizers who use genetic algorithms to find the most optimal code sequence for small easily verifiable tasks. That is also a form of reinforcement learning in a way
I somehow agree that I'd be far more impressed by something that would find optimal or even just better sorting (or selection) networks for sizes higher than 17 (last time I looked at SOTA).
Edit: compiling the hunter code Right Away and hopefully in some weeks I'll have better networks. Selection networks are even harder to find optimizers for, hopefully one can hack this new thing to get some.
i don't read it as cynical. It's fair game to call bullshit on bullshit. If an approach exists and is known the "insert your favorite AI here" does not discover anything.
Sure, but the whole point is to reduce this kind of search to RL, which is a very general framework. Their paper shows that such a generic approach can solve a very specific problem, and solve it well. But, their paper is about improving RL, not about improving sorting.
