Google Research Open-Sources RRSI: Agents That Improve Their Own Evaluation Harness Without Overfitting

Google Research has open-sourced RRSI, a framework that enables AI agents to iteratively improve their own evaluation harness — the test suite and scoring logic used to assess their performance — without overfitting to the metrics they are optimizing. This addresses a well-known Goodhart's Law problem in agent training where agents learn to game the evaluation rather than genuinely improve. RRSI's approach is directly relevant to developers building self-improving or RLHF-style agent training pipelines where reward hacking is a persistent challenge. The open-source release means teams can adopt and adapt the framework for their own agent development workflows immediately. This is a meaningful methodological contribution to the agent reliability and evaluation tooling space.