RRSI Paper: Regularization Keeps Recursive Self-Improving Agent Harnesses From Overfitting
AxSaucedo · x · 2026-10-07
A new arXiv paper, RRSI: Regularized Recursive Self-Improvement of Agent Harnesses, from a Google team (Peng Xia, Rujun Han, Huaxiu Yao, Chen-Yu Lee, et al., 15 authors).
- Context: an LLM agent's capability is largely magnified by its harness (prompts, control flow, tooling, memory, context management); recent methods automate iterative harness edits, establishing recursive self-improvement (RSI) at the agent-system level.
- Problem: recursive evolution tends to memorize training tasks — large in-distribution gains that shrink or vanish on out-of-distribution benchmarks.
- Method: RRSI adds regularization — a proposer with a temporally annealed edit budget that limits bundled changes and encourages unexplored trajectories, plus a selector with a critic (screens benchmark-specific proposals) and a pruner (removes changes that are too small, expensive, or useless).
- Results: constraints favor reusable agent mechanisms over benchmark-specific tweaks, validated across eight benchmarks.
Related event: Google's RRSI Paper Adds Regularization to Recursive Agent Self-Improvement(2 posts)→
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