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AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
61–70 of 298 posts
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#62We are entering a new era of evolutionary algorithms and LLMs. Reminds me of the idea behind: https://github.com/DivergentAI/dreamGPT
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Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#63From the paper, "Notably, for multiplying two 4 × 4 matrices, applying the algorithm of Strassen recursively results in an algorithm with 49 multiplications, which works over any field...AlphaEvolve is the first method to find an algorithm to multiply two 4 × 4 complex-valued matrices using 48 multiplications."
If you do naive matrix multiplication, you get a sense that you're doing similar work multiple times, but it's hard to quantify just what that duplicated work entails. Compare it to, for example, calculating the size of the union of two sets:
Total size = size(A) + size(B) - size(intersection(A, B))
You have to take out that extra intersection amount because you've counted it twice. What if you could avoid counting it twice in the first place? That's easy, you just iterate over each set once, keeping track of the elements you've already seen.
Strassen's algorithm keeps track of calculations that are needed later on. It's all reminiscent of dynamic programming.
What I find interesting is that it seems the extra savings requires complex values. There must be something going on in the complex plane that is again over-counting with the naive approach.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#64We are entering a new era of evolutionary algorithms and LLMs. Reminds me of the idea behind: https://github.com/DivergentAI/dreamGPT
That’s a really cool idea. I often used https://dannymator.itch.io/randomicon to come up with novel ideas, never thought of feeding random words to llm as a way of doing it.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#65Software 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…
Everyone will just turn into a problem solver until there are no more problems.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#66Cool, but don't get me wrong, isn't this essentially similar to Google's Co-Scientist, where multiple models are in a loop, passing context back and forth validating things? At its core, it's still a system of LLMs, which is impressive in execution but not fundamentally new. LLMs are undoubtedly useful at tasks like code "optimisation" and detecting patterns or redundancies that humans might overlook, but this announ…
pardon "Google's Co-Scientist" ? There are multiple projects called that?
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#67Calling 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…
Genetic algorithm is worse than gradient descent.
If variety is sought, why not beam with nice population statistic.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#68Software 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?
The thing with vague requirements is that the real problem is that making decisions is hard. There are always tradeoffs and consequences. Rarely is there a truly clear and objective decision. In the end either you or the LLM are guessing what the best option is.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#69Interestingly, they improved matrix multiplication and there was a paper on Arxiv a few days ago [1] that also improved matrix multiplication and the only case common to both is (multiplying 4x5 matrix with 5x6 matrix) and they both improved it from 93 to 90. [1]: https://arxiv.org/html/2505.05896v1
There’s been a ton of work on multiplying very large matrices. But actually, I have no idea—how well explored is the space of multiplying small matrices? I guess I assume that, like, 4x4 is done very well, and everything else is kind of… roll the dice.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#70Calling 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…
This technique doesn't actually use RL at all! There’s no policy-gradient training, value function, or self-play RL loop like in AlphaZero/AlphaTensor/AlphaDev.
As far as I can read, the weights of the LLM are not modified. They do some kind of candidate selection via evolutionary algorithms for the LLM prompt, which the LLM then remixes. This process then iterates like a typical evolutionary algorithm.