Sigh, as someone who does research in this area, this paper and its promotion on X has so many hype terms it is almost off-putting. If you read the paper what they are doing is trying to modify the scaffolding around a frozen FM until they get something better. None of this obviously includes any training (change to weights) or the underlying architecture. Even for scaffolding, a lot is still human-scaffolded: the outer loop (parent selection, evaluation protocol, task distribution) is mostly fixed. They experimented with editing parent selection and it rediscovers heuristics like UCB/softmax, but doesn’t yet beat handcrafted versions, so a lot of metrics are incremental, which is okay, that is what research is often. But it's not like a run away self-improvement or "improve forever" that people spin online.
It is an extension of their DGM paper.
Also it's ~88M+ tokens per full run I think, not surprising as any sort of exploratory search is expensive and I commend them for releasing the code online because it pushes this small subfield. But people need to temper their expectations. IMO the best part is a nice transfer between improvement objectives after exhaustive iteration that they found. I am wondering if what we have here is a way to exhaust local search space, by letting the model better express it.
On a separate one thing I think a lot about is whether these unchecked hyped claims and terms and marketing of papers actually does more bad than good to the field by setting expectations that cannot be delivered and distracting from the actual hard and unsexy nature of problems that need to be solved.