how is this new? evolutionary heuristics have been around for a long time. why give it a new name?
A deep dive into self-improving AI and the Darwin-Gödel Machine
61–70 of 103 posts
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#62This is genetic programming and is probably older than the authors. Did somebody just came up with a new term for an old concept?
exactly, thank you
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#63Earlier quoted context omitted.
There is no "gaming the system" in Darwinian evolution. You reproduce or you don't. There's no way to fail reproduction and still perpetuate your genetics.
That is not true. There are plenty of ways not to reproduce and still to perpetuate your genetics. For example, if you don't have children of your own, but support people that have similar genetic traits to your own.
What is this? Genetics for ants?
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#64ok this part kinda blew my brain open. it’s literally like you’re watching code evolve like git history on steroids. archive not pruning anything? yes. finally someone gets that dead code ain’t always dead it’s just early.
letting weaker agents still contribute? feels illegal but also exactly how dumb breakthroughs happen. like half my best scripts started as broken junk. it just kept mutating till something clicked.
and self-editing agents??? not prompts, not finetunes, straight up source code rewrites with actual tooling upgrades. like this thing bootstraps its own dev env while solving tasks.
plus the tree structure, parallel forks, fallback paths basically says ditch hill climbing and just flood the search space with chaos. and chaos actually works. they show that dip around iteration 56 and boom 70 blows past all. that’s the part traditional stuff never survives. they optimise too early and stall out. this one’s messy by design. love it.
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#65Making improvements to self hosted dialog engines/vibe coding tools was the first thing I used LLMs for seriously and that was way back when salesforce's 350m codegen model was the biggest one I could run. It's funny people have come up with a new phrase to describe this.
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#66Earlier quoted context omitted.
There is no "gaming the system" in Darwinian evolution. You reproduce or you don't. There's no way to fail reproduction and still perpetuate your genetics.
That is not true. There are plenty of ways not to reproduce and still to perpetuate your genetics. For example, if you don't have children of your own, but support people that have similar genetic traits to your own.
If they aren’t your children they aren’t your genes.
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#67Earlier quoted context omitted.
There is no "gaming the system" in Darwinian evolution. You reproduce or you don't. There's no way to fail reproduction and still perpetuate your genetics.
Consider the plumpest cows whose carcass has been noticed, then subsequently cloned.
Cloning is reproduction.
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#68Earlier quoted context omitted.
There is no "gaming the system" in Darwinian evolution. You reproduce or you don't. There's no way to fail reproduction and still perpetuate your genetics.
It is common misconception, but evolution does not happen at the individual level, but on populations, so a single individual not reproducing is irrelevant, as long as the local population carrying the same genes do successfully reproduce.
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Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#69I spent a lot of time last summer trying to get prompts to optimise using various techniques and I found that the search space was just too big to make real progress. Sure - I found a few little improvements in various iterations, but actual optimisation, not so much. So I am pretty skeptical of using such unsophisticated methods to create or improve such sophisticated artifacts.
This is exactly what I'm doing. Some papers I'm studying: TextGrad: Automatic "Differentiation" via Text: https://arxiv.org/abs/2406.07496 LLM-AutoDiff: Auto-Differentiate Any LLM Workflow : https://arxiv.org/abs/2501.16673 Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs: https://arxiv.org/abs/2406.16218 GReaTer: Gradients over Reasoning Makes Smaller Language Models…
I was trying to pick n-shot examples from a data set. The idea was that given 1000s of examples for a prompt finding a combination of n that was optimal could be advantageous, but for n's that are large then bruteforcing the combincation would be impossible... so can we find an optimal set with an efficient search?
But the problem was that the search space wasn't informative. The best 1 example didn't feature in the best 2 examples. So I couldn't optimise for 5, 6,7 examples..
Re: A deep dive into self-improving AI and the Darwin-Gödel Machine
#70For reference, the repo with the Python code from the "Darwin-Gödel Machine (DGM)" paper mentioned by the post: