MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution

2026-07-06

AI Summary

Current large language model (LLM) agents lack recursive self-improvement, as they optimize only task-specific skills without evolving their own improvement mechanisms. This work proposes a dual-timescale framework enabling agents to concurrently maintain and co-evolve both task skills and meta-skills through a unified process. For the first time, this approach achieves self-evolution of the improvement mechanism within a single framework, without requiring auxiliary models or external objectives. The meta-skill module comprises five components—Analyzer, Retriever, Allocator, Proposer, and Evolver—built upon a shared, frozen backbone, facilitating rapid optimization of task skills in a fast loop and gradual evolution of meta-skills in a slow loop. The method outperforms the strongest baselines by 23.54, 16.09, and 1.92 percentage points on the OfficeQA, SealQA, and ALFWorld benchmarks, respectively.

Abstract

Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability. However, a fixed, hand-authored skill is rarely optimal, and cannot adapt to the diversity of tasks an agent encounters. Self-improving agents address this by rewriting their own skill files from execution traces, yielding meaningful gains on challenging benchmarks. Yet such self-evolution remains non-recursive: it improves only the task skill (what the agent does) while the improvement procedure (how it improves) is authored once and held fixed. We introduce MetaSkill-Evolve, a two-timescale framework that makes agentic skill improvement recursive: every branch carries both a task skill $s$ and a branch-local meta-skill $m=(ψ,σ,α,π,\varepsilon)$ whose five components parameterise the Analyzer, Retriever, Allocator, Proposer, and Evolver agents of the improvement pipeline. Task skills evolve on a fast loop while the meta-skill evolves on a slower one under the same pipeline applied to itself, with no additional model or objective. With all five pipeline agents sharing a single frozen backbone, MetaSkill-Evolve outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks (OfficeQA, SealQA, ALFWorld), improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.

Problem

• LLM agents
• skill evolution
• recursive self-improvement
• meta-skill
• long-horizon tasks

Innovation

• recursive self-improvement
• two-timescale evolution
• meta-skill
• LLM agents
• skill evolution