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@inproceedings{zhou-etal-2026-data,
    title = "A Data-Efficient Path to Multilingual {LLM}s: Language Expansion via Post-training {PARAM}$\Delta$ Integration into Upcycled {M}o{E}",
    author = "Zhou, Hao  and
      Li, Tianhao  and
      Wang, Zhijun  and
      She, Shuaijie  and
      Wu, Linjuan  and
      Wei, Hao-Ran  and
      Yang, Baosong  and
      Chen, Jiajun  and
      Huang, Shujian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1238/",
    pages = "26888--26904",
    ISBN = "979-8-89176-390-6",
    abstract = "Expanding Large Language Models(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpart, they are plagued by a critical trade-off: mitigating parameter conflicts to preserve original abilities inevitably dilutes new language acquisition, and vice-versa. To resolve this conflict, we introduce , which upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. Alignment ability is then transferred by grafting a MoE-expanded parameter delta($\Delta_{\text{instruct}}$) to the CPT-enhanced base model, bypassing the complex alignment phase. Experiments demonstrate `s superiority even against baselines with similar FLOPs or number of parameters; it improves performance on expanded languages while effectively preserving original capabilities. We further show our approach is highly applicable across different models and Post-training deltas."
}

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        <namePart type="given">Baosong</namePart>
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    <abstract>Expanding Large Language Models(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpart, they are plagued by a critical trade-off: mitigating parameter conflicts to preserve original abilities inevitably dilutes new language acquisition, and vice-versa. To resolve this conflict, we introduce , which upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. Alignment ability is then transferred by grafting a MoE-expanded parameter delta(Δ_\textinstruct) to the CPT-enhanced base model, bypassing the complex alignment phase. Experiments demonstrate ‘s superiority even against baselines with similar FLOPs or number of parameters; it improves performance on expanded languages while effectively preserving original capabilities. We further show our approach is highly applicable across different models and Post-training deltas.</abstract>
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%0 Conference Proceedings
%T A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAMΔ Integration into Upcycled MoE
%A Zhou, Hao
%A Li, Tianhao
%A Wang, Zhijun
%A She, Shuaijie
%A Wu, Linjuan
%A Wei, Hao-Ran
%A Yang, Baosong
%A Chen, Jiajun
%A Huang, Shujian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhou-etal-2026-data
%X Expanding Large Language Models(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpart, they are plagued by a critical trade-off: mitigating parameter conflicts to preserve original abilities inevitably dilutes new language acquisition, and vice-versa. To resolve this conflict, we introduce , which upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. Alignment ability is then transferred by grafting a MoE-expanded parameter delta(Δ_\textinstruct) to the CPT-enhanced base model, bypassing the complex alignment phase. Experiments demonstrate ‘s superiority even against baselines with similar FLOPs or number of parameters; it improves performance on expanded languages while effectively preserving original capabilities. We further show our approach is highly applicable across different models and Post-training deltas.
%U https://aclanthology.org/2026.acl-long.1238/
%P 26888-26904

Markdown (Informal)

[A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE](https://aclanthology.org/2026.acl-long.1238/) (Zhou et al., ACL 2026)

ACL

Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, and Shujian Huang. 2026. A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26888–26904, San Diego, California, United States. Association for Computational Linguistics.