@inproceedings{liu-etal-2026-specialization,
title = "Specialization without Sparsity: Efficient and Expressive Split-Path Experts for {LLM} Fine-Tuning",
author = "Liu, Ganghao and
Zhou, Qin and
Wang, Zhe and
Lu, Xuehan and
Huang, Haihua and
Tong, Yunfei and
Tian, Heng",
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.975/",
pages = "19515--19527",
ISBN = "979-8-89176-395-1",
abstract = "Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specialization while maintaining low resource overhead. Split-Path Adaptive Representation Mixture-of-Experts (SparMoE) replaces discrete hard routing with a soft routing and fully-activated mixture, enabling stable optimization. Each expert is parameterized as a split-path modulation module, consisting of a scaling path that promotes expert specialization and a bias path that preserves expert-specific signals. This design significantly enhances expressive capacity while maintaining strict parameter efficiency and architectural compatibility with PEFT. Extensive evaluations on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk show that our approach consistently outperforms or matches state-of-the-art PEFT methods under comparable parameter budgets, achieving a favorable trade-off between adaptability and efficiency."
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<abstract>Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specialization while maintaining low resource overhead. Split-Path Adaptive Representation Mixture-of-Experts (SparMoE) replaces discrete hard routing with a soft routing and fully-activated mixture, enabling stable optimization. Each expert is parameterized as a split-path modulation module, consisting of a scaling path that promotes expert specialization and a bias path that preserves expert-specific signals. This design significantly enhances expressive capacity while maintaining strict parameter efficiency and architectural compatibility with PEFT. Extensive evaluations on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk show that our approach consistently outperforms or matches state-of-the-art PEFT methods under comparable parameter budgets, achieving a favorable trade-off between adaptability and efficiency.</abstract>
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%0 Conference Proceedings
%T Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning
%A Liu, Ganghao
%A Zhou, Qin
%A Wang, Zhe
%A Lu, Xuehan
%A Huang, Haihua
%A Tong, Yunfei
%A Tian, Heng
%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 liu-etal-2026-specialization
%X Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specialization while maintaining low resource overhead. Split-Path Adaptive Representation Mixture-of-Experts (SparMoE) replaces discrete hard routing with a soft routing and fully-activated mixture, enabling stable optimization. Each expert is parameterized as a split-path modulation module, consisting of a scaling path that promotes expert specialization and a bias path that preserves expert-specific signals. This design significantly enhances expressive capacity while maintaining strict parameter efficiency and architectural compatibility with PEFT. Extensive evaluations on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk show that our approach consistently outperforms or matches state-of-the-art PEFT methods under comparable parameter budgets, achieving a favorable trade-off between adaptability and efficiency.
%U https://aclanthology.org/2026.findings-acl.975/
%P 19515-19527
Markdown (Informal)
[Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning](https://aclanthology.org/2026.findings-acl.975/) (Liu et al., Findings 2026)
ACL
- Ganghao Liu, Qin Zhou, Zhe Wang, Xuehan Lu, Haihua Huang, Yunfei Tong, and Heng Tian. 2026. Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19515–19527, San Diego, California, United States. Association for Computational Linguistics.