Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents
Xing Zhang, Yanwei Cui, Guanghui Wang, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He

TL;DR
Ratchet is a minimal, self-contained loop enabling LLM agents to autonomously write, curate, and retire skills, significantly improving their performance without weight updates.
Contribution
It introduces Ratchet, a simple yet effective hygiene mechanism for self-evolving LLM agents, combining retirement, active-cap, meta-guidance, and pattern canonicalisation.
Findings
Ratchet improves pass@1 from 0.258 to 0.584 on MBPP+ hard-100.
The recipe transfers successfully to an agentic solver on SWE-bench.
Ablation studies show retirement and meta-skill guidance are essential.
Abstract
Self-evolving skill libraries, pioneered by Voyager, let frozen LLM agents accumulate reusable knowledge without weight updates, yet recent evaluation shows that LLM-authored skills deliver pp over no-skill baselines while human-curated ones deliver pp: the bottleneck is not skill authoring but lifecycle management. We introduce \textbf{Ratchet}, a single-agent loop in which a frozen LLM writes, retrieves, curates, and retires its own natural-language skills. Ratchet integrates four candidate hygiene mechanisms: outcome-driven retirement, a bounded active-cap, meta-skill authoring guidance, and pattern canonicalisation. On MBPP+ hard-100 with Claude Opus 4.7, Ratchet lifts held-out pass@1 from a baseline to a late-window rolling mean of (peak ) across 100 rounds and 3 seeds, a rolling-mean gain where the no-skill…
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