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AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

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Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#81

Post by [deleted] »

zaken wrote: post #73
moritonal wrote: post #25

For the people awaiting the singularity, lines like this written almost straight from science fiction: > By suggesting modifications in the standard language of chip designers, AlphaEvolve promotes a collaborative approach between AI and hardware engineers to accelerate the design of future specialized chips."

Honestly it's this line that did it for me: > AlphaEvolve enhanced the efficiency of Google's data centers, chip design and AI training processes — *including training the large language models underlying AlphaEvolve itself*. Singularity people have been talking for decades about AI improving itself better than humans could, and how that results in runaway compounding growth of superintelligence, and now it's here.

[deleted]

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#82

Post by kaibee »

bossyTeacher wrote: post #27
ldjkfkdsjnv wrote: post #3

Software engineering will be completely solved. Even systems like v0 are astounding in their ability to generate code, and are very primitive to whats coming. I get downvoted on HN for this opinion, but its truly going to happen. Any system that can produce code, test the code, and iterate if needed will eventually outperform humans. Add in the reinforcement learning, where they can run the code, and train the model…

What about brownfield development though? What about vague requirements or cases with multiple potential paths or cases where some technical choices might have important business consequences that shareholders might need to know about? Can we please stop pretending that software engineering happens in a vacuum?

> What about vague requirements or cases with multiple potential paths or cases where some technical choices might have important business consequences that shareholders might need to know about?

If the cost of developing the software is 0, you can just build both.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#83

Post by Workaccount2 »

nprateem wrote: post #10

Maybe this one can stop writing a fucking essay in code comments. I'm now no longer surprised just how consistently all the gemini models overcomplicate coding challenges or just plain get them wrong. Claude is just consistently spot on. A few salient comments for tricky code instead of incessantly telling me what it's changed and what I might want to do, incorrect assumptions when it has the code or is something we'…

The model likely is doing it more for itself than for you.

You can take the code and give it to another LLM instance and ask it to strip all comments.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#84

Post by moregrist »

vjerancrnjak wrote: post #67
resiros wrote: post #41

Earlier quoted context omitted.

Skimmed quickly the paper. This does not look like RL. It's a genetic algorithm. In a previous life I was working on compbio (protein structure prediction), we built 100s of such heuristic based algorithm (monte carlo simulated annealing, ga..). The moment you have a good energy function (one that provide some sort of gradient), and a fast enough sampling function (llms), you can do looots of cool optmization with su…

Genetic algorithm is worse than gradient descent. If variety is sought, why not beam with nice population statistic.

This depends quite a bit of what you’re trying to optimize.

Gradient descent is literally following the negative of the gradient to minimize a function. It requires a continuous domain, either analytical or numerical derivatives of the cost function, and has well-known issues in narrow valleys and other complex landscapes.

It’s also a local minimization technique and cannot escape local minima by itself.

_Stochastic_ gradient descent and related techniques can overcome some of these difficulties, but are still more or less local minimization techniques and require differentiable and continuous scoring functions.

In contrast, genetic algorithms try to find global minima, do not require differentiable scoring functions, and can operate on both continuous and discrete domains. They have their own disadvantages.

Different techniques for different problems. The field of numerical optimization is vast and ancient for a reason.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#85

Post by spyckie2 »

xianshou wrote: post #7

Calling it now - RL finally "just works" for any domain where answers are easily verifiable. Verifiability was always a prerequisite, but the difference from prior generations (not just AlphaGo, but any nontrivial RL process prior to roughly mid-2024) is that the reasoning traces and/or intermediate steps can be open-ended with potentially infinite branching, no clear notion of "steps" or nodes and edges in the game…

I think you mean the general class of algorithms that scale with compute times, RL being the chief example. But yes I agree to that point.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#86

Post by spyckie2 »

modeless wrote: post #28

Interesting that this wasn't tested on ARC-AGI. Francois has always said he believed program search of this type was the key to solving it. It seems like potentially this approach could do very well.

My thought as well. How well does it translate into arc agi? If it does well then we have a general purpose super intelligence… so maybe agi?

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#87

Post by dmos62 »

simianwords wrote: post #77

I'm surprised I'm not able to find this out - can some one tell me whether AlphaEvolve involves backprop or not? I honestly have no idea how AlphaEvolve works - does it work purely on the text level? Meaning I might be able to come up with something like AlphaEvolve with some EC2's and a Gemini API access?

No, the program and prompt databases use a genetic algorithm.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#88

Post by boshalfoshal »

lawlessone wrote: post #46
boshalfoshal wrote: post #43

Earlier quoted context omitted.

AI will indeed kill the leetcode interview - because once it replaces human SWEs you don't really need to give leetcode-style brainteasers to any human anymore.

You never needed to.

Yes, and you will never need to.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#89

Post by UncleOxidant »

resiros wrote: post #41
xianshou wrote: post #7

Calling it now - RL finally "just works" for any domain where answers are easily verifiable. Verifiability was always a prerequisite, but the difference from prior generations (not just AlphaGo, but any nontrivial RL process prior to roughly mid-2024) is that the reasoning traces and/or intermediate steps can be open-ended with potentially infinite branching, no clear notion of "steps" or nodes and edges in the game…

Skimmed quickly the paper. This does not look like RL. It's a genetic algorithm. In a previous life I was working on compbio (protein structure prediction), we built 100s of such heuristic based algorithm (monte carlo simulated annealing, ga..). The moment you have a good energy function (one that provide some sort of gradient), and a fast enough sampling function (llms), you can do looots of cool optmization with su…

> This does not look like RL. It's a genetic algorithm.

couldn't you say that if you squint hard enough, GA looks like a category of RL? There are certainly a lot of similarities, the main difference being how each new population of solutions is generated. Would not at all be surprised that they're using a GA/RL hybrid.

Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

#90

Post by dpflan »

Amekedl wrote: post #50
jasonjmcghee wrote: post #16

> AlphaEvolve achieved up to a 32.5% speedup for the FlashAttention kernel implementation in Transformer-based AI models > In roughly 75% of cases, it rediscovered state-of-the-art solutions, to the best of our knowledge. > And in 20% of cases, AlphaEvolve improved the previously best known solutions These sound like incredible results. I'd be curious what kind of improvements were made / what the improvements were.…

I'm thinking reading numbers like this is really just slop lately. FA achieving a 32.5% speed up? Cool. Why not submit it as a PR to the Flash Attention repo then? Can I read about it more in detail?

I have not read this linked article, but your comment made me recall a discussion about a speed up of CUDA kernels presented by Sakana AI Labs. The researcher Ravid Shwartz Ziv at NYU posted about it on LinkedIn [1], and here is the Twitter post of interest [2]

""" Yesterday's news about Sakana AI Labs provided an important lesson for all of us working with AI agents. Their announcement of an AI system that could supposedly optimize CUDA kernels to run 100x faster initially seemed like exactly the kind of use cases we've been hoping for in AI-assisted development.

Like many others, I was excited about it. After all, isn't this exactly what we want AI to do - help us optimize and improve our technical systems?

However, careful investigation by the community (on Twitter) revealed a different story. What really happened? The AI-generated CUDA kernel appeared to achieve incredible speedups, but the code was inadvertently reusing memory buffers containing previous results, essentially bypassing the actual computation. When properly evaluated, the kernel actually runs about 3x slower than the baseline. """

[1] https://www.linkedin.com/posts/ravid-shwartz-ziv-8bb18761_ye...

[2] https://x.com/main_horse/status/1892473238036631908

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