Skill Weaving: Efficient LLM Improvement via Modular Skillpacks

📰 ArXiv cs.AI

Learn to improve LLMs using SkillWeave, a modular framework that enables specialization under fixed memory budgets

advanced Published 23 May 2026

Action Steps

Partition a general-purpose LLM into skillpacks using SkillWeave
Develop lightweight, domain-specific delta modules for each skillpack
Integrate skillpacks into the LLM while maintaining a fixed memory budget
Evaluate the improved LLM using domain-specific benchmarks
Refine the skillpacks through iterative fine-tuning and testing

Who Needs to Know This

AI engineers and researchers can benefit from this technique to enhance LLM performance in specific domains without sacrificing overall capacity

Key Insight

💡 Modular skillpacks can efficiently improve LLMs in specific domains without sacrificing overall capacity

Key Takeaways

Learn to improve LLMs using SkillWeave, a modular framework that enables specialization under fixed memory budgets

Full Article

Title: Skill Weaving: Efficient LLM Improvement via Modular Skillpacks

Abstract:
arXiv:2605.22205v1 Announce Type: new Abstract: Large language models increasingly require specialization across diverse domains, yet existing approaches struggle to balance multi-domain capacities with strict memory and inference constraints. In this work, we introduce SkillWeave, a modular improvement framework that enables LLMs to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into skillpacks -- lightweight, domain-specific delta modu

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