It seems to me this is only useful for self-improvement up to the point of accomplishing objectives and problems that humans have already clearly defined, solved and mapped. For example, "At checkpoints, a stronger evaluator can replace the old one if it performs better on trusted ground-truth examples." - if they're a trusted ground-truth, they must be rigorous. If we're attempting to solve problems that humans have not already solved, where do you get your ground-truth examples? It's not like the AI is going to be able to generate these for you if it has never seen a solution to the problem.

I can definitely see the argument that this allows us to train models faster and converge faster, because if you scale difficulty of evaluation alongside the learners capabilities, it spends a lot less time floudering around. It basically works out to be loss minimization through strategic ordering of the training data. Is that the goal here though? Or is the goal recursive self-improvement and solving problems that are currently outside of reach? Because it doesn't feel like the latter would be possible with this design.

For example, how do you quantify "the evaluation gets -harder- as the agent gets better". Harder, how? In what direction? Via what criteria or measure?

Here’s a paper by Floreano at EPFL from 1997 explicitly on Red Queen dynamics for creating neural networks for intelligent robot control.

There was lots of discussion of these ideas in the 1990s. In those days we trained very small NNs - tens of nodes - by evolving their weights and topologies. A run could take days on a workstation of the time.

This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.

https://infoscience.epfl.ch/entities/publication/a65d0679-68...

Have not read the paper yet, but this not sound like GAN applied to agent training?

I presume the chess image in the piece is AI generated because it makes no sense if you’re even vaguely familiar with the source material.

I wonder if this would work for generating algorithmic code for a town of NPC's in a RPG or city sim. The problem the teacher would be tasked to solve, in this case, would be to score NPC algorithms on whether they would lead to happiness, health, and wealth for their character. This would allow the generation of NPC algorithms to run at faster pace then would normally be possible if you had to simulate their lives for a day, a week, or a month just to see if their algorithm would be a success or not. And since NPC's are competing against each other, they would naturally need more sophisticated algorithms to succeed.

This 'new' method was quite common in evolutionary computing in the 90's.

This does sound very interesting, and my immediate thought on this would be to have 2 or multiple agents in learning, where they continuously set a new bar among all of them. It might be that it will just converge towards them all becoming more similar as they would raise bar by what they know they perform better at than the other. So it might be necessary to find some heuristic to the measurement to avoid that route to be taken.

> "Instead of improving an agent against a fixed test, we let the evaluation evolve alongside the agent"

This quote should have been highlighted earlier in the article.

> The research team, which includes collaborators from NVIDIA and Flower Labs, have come up with a new method for recursive self-improving AI agents to continue improving themselves.

What happens if you apply the method to non-recursive self-improving AI agents? Can they continue improving themselves? Or does the recursive self improvement only recursively self improve AI agents which are themselves recursively self-improving?

The ultimate goal is survival.

There was another over weekend were in similar setup, the agents tried to sabotage each other.

"trusted ground truth"

Reads like "... and then draw the rest of the fucking owl"

Yeah I think it is broadly applicable to tech as a whole, I mean any great startup is just really a counter positioned company to incumbents -

You're all going to die down here.