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?
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.