For broader context: our work directly builds upon Jürgen Schmidhuber’s Huxley-Gödel Machine, while our research has been cited in a recent survey he co-authored: https://arxiv.org/abs/2607.13104
Anyone interested in this field should definitely engage thoroughly with his body of work.
I by no means wanted to suggest the opposite (that's how I found the connection)!
Schmidthuber is famous for his groundbreaking work in machine learning, but also for being somewhat left out when people list the "Grandparents of Deep Learning" and for being vocal about that. I just wanted to poke that bit. If your work takes off, it would be a great chance for him to shine.
No worries, I just wanted to give proper credit in the comment above. The blog post lacks the broader context and references of the paper, and Schmidhuber and his students deserve recognition. I edited the comment to make this clearer.
On this topic, I have been pleasantly surprised to see some of the works he has recently supervised receive direct recognition, particularly the Huxley-Gödel Machine and its many siblings in the field of self-improving AI: https://github.com/metauto-ai
For tasks with no existing ground-truth data, one could try to use an adversarial objective on its own by making AI-generated outputs compete against each other, although this is beyond the scope of our experiments. Adapting our method to this setting is quite straightforward.
For now, we have looked into composite objectives trading off performance on the ground truth against the ability to reject generated samples produced in earlier epochs. For example, when using this system to co-evolve paper writers and reviewers, we reuse generated papers that were accepted by the reviewer of epoch t as adversarial samples in epoch t+1. Then, new reviewers are rewarded for rejecting AI-generated papers. You can have ground-truth performance account for x of the utility of a reviewer, with the adversarial objective then providing 1-x.
The paper is explicitly an application of a very general evolutionary principle to the self-improving tree search algorithms popularised by the Huxley-Gödel Machine and Darwin-Gödel Machine.
We agree that the methods used have been extensively researched across a wide variety of domains.
Anyone interested in this field should definitely engage thoroughly with his body of work.