@inproceedings{li-etal-2026-skill,
title = "Skill Weaving: Efficient {LLM} Improvement via Modular Skillpacks",
author = "Li, Zhuo and
DU, Guodong and
Shi, Zesheng and
Guo, Weiyang and
Yao, Weijun and
Zhou, Yuan and
Zhang, Jiabo and
Li, Jing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1989/",
pages = "40000--40023",
ISBN = "979-8-89176-395-1",
abstract = "In this work, we introduce SkillWeave, a modular improvement framework that enables large language models to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into domain-specific skillpacks{---}lightweight, domain-specific delta modules{---}that reorganize and refine the model{'}s internal knowledge. To ensure deployment efficiency, SkillWeave incorporates SkillZip, a compression component that transforms specialized parameters into lightweight, inference-ready skillpacks. Together, these components allow SkillWeave to achieve strong multi-domain performance and inference-efficient execution. On multi-task and agentic benchmarks, a 9B SkillWeave model outperforms task-specific baselines and even surpasses a 32B monolithic LLM, while achieving up to 4{\texttimes} speedup."
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<abstract>In this work, we introduce SkillWeave, a modular improvement framework that enables large language models to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into domain-specific skillpacks—lightweight, domain-specific delta modules—that reorganize and refine the model’s internal knowledge. To ensure deployment efficiency, SkillWeave incorporates SkillZip, a compression component that transforms specialized parameters into lightweight, inference-ready skillpacks. Together, these components allow SkillWeave to achieve strong multi-domain performance and inference-efficient execution. On multi-task and agentic benchmarks, a 9B SkillWeave model outperforms task-specific baselines and even surpasses a 32B monolithic LLM, while achieving up to 4× speedup.</abstract>
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%0 Conference Proceedings
%T Skill Weaving: Efficient LLM Improvement via Modular Skillpacks
%A Li, Zhuo
%A DU, Guodong
%A Shi, Zesheng
%A Guo, Weiyang
%A Yao, Weijun
%A Zhou, Yuan
%A Zhang, Jiabo
%A Li, Jing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-skill
%X In this work, we introduce SkillWeave, a modular improvement framework that enables large language models to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into domain-specific skillpacks—lightweight, domain-specific delta modules—that reorganize and refine the model’s internal knowledge. To ensure deployment efficiency, SkillWeave incorporates SkillZip, a compression component that transforms specialized parameters into lightweight, inference-ready skillpacks. Together, these components allow SkillWeave to achieve strong multi-domain performance and inference-efficient execution. On multi-task and agentic benchmarks, a 9B SkillWeave model outperforms task-specific baselines and even surpasses a 32B monolithic LLM, while achieving up to 4× speedup.
%U https://aclanthology.org/2026.findings-acl.1989/
%P 40000-40023
Markdown (Informal)
[Skill Weaving: Efficient LLM Improvement via Modular Skillpacks](https://aclanthology.org/2026.findings-acl.1989/) (Li et al., Findings 2026)
ACL
- Zhuo Li, Guodong DU, Zesheng Shi, Weiyang Guo, Weijun Yao, Yuan Zhou, Jiabo Zhang, and Jing Li. 2026. Skill Weaving: Efficient LLM Improvement via Modular Skillpacks. In Findings of the Association for Computational Linguistics: ACL 2026, pages 40000–40023, San Diego, California, United States. Association for Computational Linguistics.