Google researchers propose RRSI to prevent self-improving agents from memorizing test tasks

Google researchers propose RRSI (Regularized Recursive Self-Improvement of Agent Harnesses), adding constraints to the self-improvement loop where language models repeatedly rewrite the agent harness, to prevent the agent from memorizing test tasks. The paper notes that self-improvement causes the agent to memorize limited test tasks, with training scores rising but gains on new tasks diminishing or disappearing, due to reasons including search remembering patterns that only fit a single benchmark, preferring candidates with coincidental high scores, and stacking complexity that does not aid capability. RRSI imposes limits on both ends: proposing changes and filtering changes. An edit budget limits the number of independent changes a candidate can bundle at once and shrinks over time; a critic rejects schemes that hardcode task names, answers, or other benchmark-specific tricks; higher compute is accepted only if it brings measurable performance gains; and ineffective components are removed. The authors note the research only covers harnesses based on frozen models, not cases where model weights change, and the code has been released on GitHub.