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

תקציר מקורי באנגליתGoogle Cloud AI Research , with UNC-Chapel Hill, Stanford and Washington University in St. Louis, has released RRSI (Regularized Recursive Self-Improvement) . It lets an LLM agent rewrite its own harness: prompts, tools, memory, control flow and sub-agents. Model weights never change. RRSI constrains the improvement loop itself, so gains hold on benchmarks the agent never optimized against. Deployable? Yes, as a research framework. The code is Apache 2.0 , needs Python 3.10+, and accepts any LiteLLM model string. Defaults assume Claude Opus 4.8 on Vertex AI. Why Self-Improving Harnesses Overfit Harness evolution loops propose edits, score them on a fixed evolve set and keep the winner. The same tasks are reused every round, so the loop can memorize them. The RRSI research names 3 failure m