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A new paper from Cambridge, NVIDIA, and other labs introduces the Red Queen Gödel Machine, a method where AI agents and their evaluators co-evolve to prevent stagnation. The approach avoids fixed benchmarks by allowing judges to improve at safe handoff points, leading to better performance in coding and paper writing tasks.
The Red Queen Gödel Machine paper from NVIDIA, Cambridge University, and other teams solves the bottleneck of recursive self-improvement by co-evolving agents and evaluators. It surpasses existing SOTA on tasks like code and paper writing, providing an important methodology for controlled open-ended AI evolution.
Promotes a one-hour Cambridge lecture by Demis Hassabis that provides deep insights into the future of AI, claiming it will teach more than most learn in five years.