The Red Queen hypothesis - a new way forward for self-improving AI
Researchers in Cambridge's Department of Computer Science and Technology, with collaborators from NVIDIA, Flower Labs, MBZUAI and Inria, start from a problem team member Alex Iacob puts plainly: "A self-improving agent can only get as good as the test that scores it." Their Red Queen Gödel Machine improves the evaluator as well. The evaluator is kept fixed within each phase so progress can be measured, and at checkpoints a stronger one replaces it if it does better on trusted ground-truth examples. Across scientific papers and Olympiad-level proofs, co-evolved paper writers reached 1.78 to 1.86 times higher acceptance rates under a panel of AI judges (not at a real conference), and co-evolved graders 9% higher ground-truth accuracy. The team calls the work preliminary, and the page says the method will be open sourced.
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