Google proposes RRSI to prevent overfitting in agent self-optimisation
What happened
On 4 October 2026, The Decoder reported that Google researchers have proposed RRSI (Regularized Recursive Self-Improvement of Agent Harnesses). The work addresses a problem in agent self-optimisation: when agents improve themselves, they memorise test tasks, so the gains disappear on new tasks, i.e. overfitting occurs. RRSI aims to regularise this process to prevent such memorisation of test tasks. The report so far discloses only the method's name, the proposing organisation and the problem it targets, with no technical details or experimental results.
Written by AI from the reports · updated 12 d ago
Timeline
Follow the reports to see the event from each side.
- The DecoderGoogle researchers find a way to keep self-improving AI agents from memorizing their tests
Self-improving AI agents tend to memorize their test tasks, so their gains shrink or disappear on new ones. RRSI, a new method from Google researchers, reins in this effect and lifts scores on unseen benchmarks by up to 4.7 points while using about 30 percent fewer tokens than an unregularized version.