SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents

2026-05-28

AI Summary

Current large language model agents often expand their skill libraries in a strictly additive manner, leading to the accumulation of redundant, outdated, or even harmful skills due to a lack of effective management. This work addresses this issue by conceptualizing the skill library as an entity requiring active refinement and introduces SkillBrew, a Pareto-aware multi-objective optimization framework. SkillBrew employs a two-stage “propose-and-verify” mechanism to dynamically balance utility, diversity, and query coverage during skill library evolution. By integrating retrieval-augmented large language models for skill evaluation and selection, the method significantly enhances skill library quality on two public benchmarks, offering a crucial step toward self-improving agents capable of maintaining efficient and adaptive skill sets.

Abstract

Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches typically expand these banks in an append-only fashion, continuously adding new skills without removing redundant, outdated, or harmful ones, resulting in inefficient and poorly curated repositories. In this paper, we formulate the skill bank curation as a constrained multi-objective problem: a desirable bank must be useful for the agent, diverse in its content, and provide good coverage of the query distribution. To this end, we introduce SkillBrew, a multi-objective curation framework that formalizes skill bank curation as Pareto-aware optimization under a utility constraint, and solves it via a bi-level propose-then-verify loop. We evaluate our approach on two public benchmarks. Our findings suggest that treating skill banks as objects of principled curation, rather than ever-growing append-only logs, is an important step toward building self-improving LLM agents.

Problem

• skill bank curation
• retrieval-augmented LLM agents
• multi-objective optimization
• Pareto-aware optimization
• self-improving agents

Innovation

• multi-objective optimization
• skill bank curation
• retrieval-augmented LLM agents
• Pareto-aware optimization
• bi-level propose-then-verify