Google's RRSI method stops self-improving agents from memorizing tests

Self-improving AI agents tend to memorize their test tasks, so gains shrink on new ones. Google researchers' RRSI method regularizes this effect. It lifts scores on unseen benchmarks by up to 4.7 points and uses about 30 percent fewer tokens than an unregularized version.

Why it matters: Overfitting to the evaluation set is the central credibility problem for recursive self-improvement: gains that vanish on unseen tasks are not real capability. Paired with DeepMind's Dream-RSI work on cutting search iterations, this suggests labs are now focused on making self-improvement loops both cheaper and honest, which matters as agents increasingly tune themselves.