Google's RRSI stops self-improving AI agents from memorizing tests, lifting unseen benchmarks by 4.7 points
The Decoder · rss · 2026-10-04
Self-improving AI agents tend to memorize their test tasks, causing gains that shrink or vanish on new ones.
Google researchers propose RRSI, a regularization method that reins in this memorization effect, boosting scores on unseen benchmarks by up to 4.7 points while using about 30% fewer tokens than an unregularized baseline.
The work highlights a key pitfall of self-improvement training: apparent progress may just be overfitting to the test set rather than genuine capability gains.
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