Google Research Open-Sources RRSI: AI Agents That Improve Their Own Harness Without Overfitting
MarkTechPost·4 min read
Google 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 modes: benchmark-specific fitting, noise chasing and complexity accumulation. Each one widens the gap between evolve-set scores and real transfer.
How RRSI Works
RRSI keeps every harness component editable. It regularizes how the search moves instead.
Proposal side
Annealed edit budget: a cosine schedule lets early rounds bundle several edits. Late rounds allow a single attributable change.
Evidence-aware credit: each candidate is logged with its component, hypothesis, diff, score change and cost change. The proposer reads this ledger, so falsified ideas are not retried.
Structured exploration: when progress stalls inside the noise band, budget shifts to components the run never touched.
Selection side
Leakage critic: rejects task names, entities, answers or benchmark-specific logic before any scoring.
Noise-adjusted floor: gains must clear the variance measured on the unchanged base harness.
Cost rule: extra inference tokens must be paid for by measured gain.
Pruning: components that stop producing gains become deletion targets.
The research team frame these as analogies to classic regularizers. The edit budget maps to L0, pruning to Lasso (L1) and the cost rule to Ridge (L2).
Harvey LAB: +1.1 on the evolve split, +2.3 on its held-out split.
All 6 held-out splits improved. With Gemini 3.5 Flash as the policy, Terminal-Bench 2.1 rose from 64.6 to 78.7. SWE-bench Verified rose from 76.8 to 79.0.
The harness is also lighter. On the agentic workspace instance, RRSI uses 2.42M policy tokens per trial. Unregularized evolution uses 3.80M. The abstract reports this as 30% fewer; the project page says 36%.
RRSI vs Closest Competitors
Scores come from Table 1 of the RRSI research paper. All methods share the same starting harness, policy, evolve split and candidate budget.
bmax (edits per candidate, early) 5
bmin (edits per candidate, late) 1
T (rounds) 16
Early rounds may bundle coordinated edits to find a mechanism. Late rounds get single, attributable changes.
round 0round T−1
Cosine schedule from the paper (Eq. 4). Slider values are for exploration; the paper lists its own settings in Appendix D.
ΔS, score gain vs incumbent (pts) 2.0
ΔC, token cost change (%) 20
δ, noise band from repeated base runs (pts) 1.5
β0 + β1ΔS cost allowance: β1 (% per pt) 8
?Noise-adjusted floor: score must not drop more than δ below the best so far
Illustrative thresholds (β0 fixed at 5% here). In RRSI, δ is estimated on the unchanged base harness and β values are tuned on the evolve set, then frozen.
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{t:'H0 vs RRSI, frozen Claude Opus 4.8 (evolve split marked)',max:100,u:'',rows:[
['Terminal-Bench 2.1','evolve',74.2,80.2],['SWE-bench Verified','OOD',82.0,83.8],['Harvey LAB','evolve',89.4,90.5],['Harvey LAB held-out','ID held-out',86.9,89.2],['JobBench','OOD',36.0,40.7],['GDPval','OOD',48.8,52.3],['APEX-Agents','OOD',34.2,37.9],['EngDesign','evolve',50.0,54.9],['Frontier-Eng','OOD',17.7,22.0]],
n:'Grey bar = unevolved harness H0, blue bar = RRSI. Frontier-Eng is Medal points; GDPval is win rate vs human experts; Harvey LAB is rubric criteria passed.'},
{t:'Out-of-distribution average (JobBench, GDPval, APEX-Agents)',max:50,u:'',single:1,rows:[['H0 (no evolution)','',39.7],['Meta-Harness','',40.6],['AHE','',39.2],['TTHE','',38.0],['HarnessX','',39.7],['RRSI','',43.6]],
n:'Averages computed from Table 1 of the RRSI paper. All methods start from the same H0, policy, evolve split and candidate budget.'},
{t:'Policy tokens per trial (millions), agentic workspace',max:4,u:'M',single:1,rows:[['H0 (no evolution)','OOD 39.7',1.56],['Unregularized evolution','OOD 40.3',3.80],['w/o acceptance regularizers','OOD 41.0',3.59],['w/o proposal regularizers','OOD 41.9',2.69],['RRSI','OOD 43.6',2.42]],
n:'From the ablation table: RRSI uses 2.42M tokens per trial vs 3.80M for unregularized evolution, about 36% fewer, with the best OOD average.'}],view=0;
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