Google introduces RRSI to prevent agent overfitting
Researchers from Google Cloud AI have introduced RRSI (Regularized Recursive Self-Improvement), a framework designed to prevent adaptive overfitting when autonomously optimizing LLM agent harnesses. By combining a temporally annealed proposal budget with critic and pruner selectors, RRSI achieved up to 14.1-point gains on training benchmarks and 4.7-point improvements on unseen tasks while cutting token overhead by 30%.
Autonomous harness optimization is the next frontier of agent engineering, but unconstrained recursive improvement inevitably turns into automated Goodhart's Law.
• Prevents eval-gaming: By screening proposed scaffolding edits with a critic and pruner, RRSI ensures that agent modifications represent generalizable workflows rather than benchmark-memorized shortcuts.
• Efficiency alongside performance: Slashing token usage by 30% demonstrates that agent performance often degrades due to prompt bloat and unnecessary scaffolding rather than insufficient instructions.
• Crucial for closed-source stacks: Because it operates entirely outside the underlying model weights, regularized harness evolution provides an effective optimization vector for enterprises building on closed APIs.
DISCOVERED
1h ago
2026-09-23
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2h ago
2026-09-23
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