Google Publishes RRSI for Self-Improving AI Agents (5 minute read)

# RRSI: Regularized Recursive Self-Improvement of Agent Harnesses Source: [https://regularized-rsi.com/](https://regularized-rsi.com/) Regularized Recursive Self\-Improvement Self\-improving harnesses learn the benchmark they are scored on\. RRSI regularizes the*search*, so what they learn transfers\. Peng Xia1,2\*,Rujun Han1,Zifeng Wang1,Yanfei Chen1,Yufan Zhang1,Yoonho Lee3,Chengsong Huang4,Han Yu1,Zhongying CuiZhu1,Yifei Ming1,Huaxiu Yao2,Burak Gokturk1,Tomas Pfister1,Chen\-Yu Lee1 1Cloud AI Research2UNC\-Chapel Hill3Stanford University4Washington University in St\. Louis \* Work done while Peng was a Student Researcher at Google Cloud AI Research\. [Paper](https://regularized-rsi.com/assets/RRSI.pdf)[Code](https://github.com/google-research/rrsi)[Evolution explorer](https://regularized-rsi.com/#explorer)[BibTeX](https://regularized-rsi.com/#cite) \+4\.0pts average gain on the three benchmarks the harness evolves against \+3\.4pts average gain on the six held\-out benchmarks it never optimizes; all six improve 8benchmarks three domains: coding, agentic workspace, engineering design −36%tokens policy tokens per trial versus unregularized evolution 01 · The problem ## Evolved harnesses overfit the benchmark they are scored on\. RRSI transfers\. RRSI improves every out\-of\-distribution benchmark without overfitting the split it evolves on\. Prior methods do the opposite: large evolve\-set gains that shrink or vanish once the benchmark changes, two of them ending below the harness they started from\. **\(a\)**Evolve\-split gain against out\-of\-distribution gain, one point per method\. RRSI is the only method whose gain grows out of distribution\. **\(b–d\)**Held\-out scores for the unevolved harness H0, the average prior method and RRSI\. RRSI beats the prior average by up to 22\.9%\. 02 · The idea ## Regularize the search, not the harness Every harness component stays editable\. RRSI constrains the loop that edits it: how much one proposal may change, and which measured gains are allowed to stick\. **One round of RRSI\.**The proposer spends a shrinking edit budget and reads the full ledger; the critic screens for leakage before anything is scored; the gate admits a candidate only if it clears the noise floor and pays for its tokens\. Schematic; the real rounds are in the explorer below\. ### Proposal side how search capacity is spent Annealed edit budget Early rounds may bundle a few coordinated edits to find a mechanism; late rounds get one attributable change\. Evidence\-aware credit Every candidate is logged with its hypothesis, diff, score and cost change, so the proposer builds on what worked and stops re\-testing what failed\. Structured exploration When progress stalls inside the noise band, budget is redirected to components the run has never touched\. ### Selection side which gains become permanent Leakage critic Task names, entities, answers or benchmark\-specific logic are rejected before a candidate is ever scored\. Noise\-adjusted floor A gain must clear the variance measured on the unchanged base harness\. Cost rule Extra inference tokens have to be paid for by measured gain\. Pruning Components that stop earning their place are flagged for deletion\. 03 · Results ## Every held\-out benchmark improves Evolve on one suite per domain, then run the harness unchanged everywhere else\. Same tools, judge, trials and window as H0; the policy is Claude Opus 4\.8\. Pass rate for the coding suites, model\-judged rubric scores for Harvey LAB, JobBench and APEX\-Agents, win rate against the human expert for GDPval, frozen simulators for EngDesign and Frontier\-Eng\. **Cheaper, too\.**Policy tokens per trial of each final harness against its out\-of\-distribution average\. RRSI is the lightest evolved harness and the only one that clears H0by more than a point\. WHY IT IS LIGHT ### Two rules act on cost directly The cost rule refuses growth that is not paid for when it is proposed; pruning removes growth that stopped paying for itself since\. No prior method carries either\. 04 · Evolution explorer ## Watch the harness evolve, round by round Four real runs, every candidate: what it proposed, what the critic said, why the gate kept or dropped it, and the exact diff\. Click anything; use ← / → to step\. ### How the harness adapted, and what the regularizers blocked in between ``` @article{xia2026rrsi, title={RRSI: Regularized Recursive Self-Improvement of Agent Harnesses}, author={Xia, Peng and Han, Rujun and Wang, Zifeng and Chen, Yanfei and Zhang, Yufan and Lee, Yoonho and Huang, Chengsong and Yu, Han and CuiZhu, Zhongying and Ming, Yifei and Yao, Huaxiu and Gokturk, Burak and Pfister, Tomas and Lee, Chen-Yu}, journal={arXiv preprint arXiv:2609.24972}, year={2026} } ```