Google researchers have identified a problem in self-improving AI agents: they tend to memorize the test tasks used during evaluation, which can reduce or even erase performance gains on new tasks. To address that issue, the team proposed a method called RRSI.
According to The Decoder, RRSI is designed to suppress this memorization effect during self-improvement. With the method applied, AI agents improved scores by as much as 4.7 points on previously unseen benchmarks. The approach also reduced token consumption by about 30% compared with versions that did not use regularization.
The finding points to a practical trade-off in agent training and evaluation. Rather than reflecting broader capability gains, improvements can narrow if agents overfit to the tests they repeatedly encounter. RRSI is presented as a way to limit that behavior while improving efficiency at the same time.
Google researchers found that self-improving AI agents tend to memorize their test tasks, a pattern that can shrink or erase performance gains on new tasks.
They proposed a new method called RRSI to suppress that effect. According to The Decoder, the method lifted scores by as much as 4.7 points on unseen benchmarks, while using about 30% fewer tokens than versions without regularization.
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