RRSI: Regularized Recursive Self-Improvement of Agent Harnesses
Google Research's RRSI regularizes recursive agent-harness edits, lifting held-out scores while cutting policy tokens.
Google Research introduces Regularized Recursive Self-Improvement (RRSI) to constrain how LLM agent harnesses evolve their prompts, tooling, memory, and control flow. An annealed edit budget and history-based exploration limit candidate changes, while a critic and pruner reject benchmark-specific, tiny, expensive, or obsolete edits. Across eight benchmarks, RRSI gains up to 14.1 points in distribution and 4.7 on five out-of-distribution tests, using 30% fewer policy tokens than unregularized evolution.
- RRSI anneals edit budgets and favors unexplored harness-evolution trajectories.
- A critic screens benchmark-specific proposals and a pruner drops weak or costly changes.
- Gains reach 14.1 in-distribution points and 4.7 on five held-out benchmarks.
- The resulting harness uses 30% fewer policy tokens than unregularized evolution.
- Evaluated across eight coding, workspace, and engineering-design benchmarks.
- Organizations
- Google Research
From the original article
1 min read at arxiv.orgAn LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically establishing a form of recursive self-improvement (RSI) at the agent-system level.
By Peng Xia · arxiv.org
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