@inproceedings{zhang-etal-2025-bts,
title = "{BTS}: Harmonizing Specialized Experts into a Generalist {LLM}",
author = "Zhang, Qizhen and
Bhargava, Prajjwal and
Bi, Chloe and
Cai, Chris X. and
Foerster, Jakob Nicolaus and
Fu, Jeremy and
Koura, Punit Singh and
Silva, Ruan and
Shen, Sheng and
Dinan, Emily and
Gururangan, Suchin and
Lewis, Mike",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.347/",
pages = "6827--6845",
ISBN = "979-8-89176-332-6",
abstract = "We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model. Following Li et al., we start with a single seed language model which is branched into domain-specific (e.g., coding or math) experts with continual pretraining. BTS combines experts into a generalist model using lightweight stitch layers, which are inserted between frozen experts and the seed LLM, and trained on a small datamix of the expert domains. Stitch layers enable the seed LLM to integrate representations from any number of experts during the forward pass, allowing it to generalize to new domains, despite remaining frozen. Because BTS does not alter the constituent LLMs, BTS provides a modular and flexible approach: experts can be easily removed and new experts can be added with only a small amount of training. Compared to alternative model merging approaches, BTS yields the best generalist performance on a variety of downstream tasks, retaining the specialized capabilities of each of the experts."
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<abstract>We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model. Following Li et al., we start with a single seed language model which is branched into domain-specific (e.g., coding or math) experts with continual pretraining. BTS combines experts into a generalist model using lightweight stitch layers, which are inserted between frozen experts and the seed LLM, and trained on a small datamix of the expert domains. Stitch layers enable the seed LLM to integrate representations from any number of experts during the forward pass, allowing it to generalize to new domains, despite remaining frozen. Because BTS does not alter the constituent LLMs, BTS provides a modular and flexible approach: experts can be easily removed and new experts can be added with only a small amount of training. Compared to alternative model merging approaches, BTS yields the best generalist performance on a variety of downstream tasks, retaining the specialized capabilities of each of the experts.</abstract>
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%0 Conference Proceedings
%T BTS: Harmonizing Specialized Experts into a Generalist LLM
%A Zhang, Qizhen
%A Bhargava, Prajjwal
%A Bi, Chloe
%A Cai, Chris X.
%A Foerster, Jakob Nicolaus
%A Fu, Jeremy
%A Koura, Punit Singh
%A Silva, Ruan
%A Shen, Sheng
%A Dinan, Emily
%A Gururangan, Suchin
%A Lewis, Mike
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-bts
%X We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model. Following Li et al., we start with a single seed language model which is branched into domain-specific (e.g., coding or math) experts with continual pretraining. BTS combines experts into a generalist model using lightweight stitch layers, which are inserted between frozen experts and the seed LLM, and trained on a small datamix of the expert domains. Stitch layers enable the seed LLM to integrate representations from any number of experts during the forward pass, allowing it to generalize to new domains, despite remaining frozen. Because BTS does not alter the constituent LLMs, BTS provides a modular and flexible approach: experts can be easily removed and new experts can be added with only a small amount of training. Compared to alternative model merging approaches, BTS yields the best generalist performance on a variety of downstream tasks, retaining the specialized capabilities of each of the experts.
%U https://aclanthology.org/2025.emnlp-main.347/
%P 6827-6845
Markdown (Informal)
[BTS: Harmonizing Specialized Experts into a Generalist LLM](https://aclanthology.org/2025.emnlp-main.347/) (Zhang et al., EMNLP 2025)
ACL
- Qizhen Zhang, Prajjwal Bhargava, Chloe Bi, Chris X. Cai, Jakob Nicolaus Foerster, Jeremy Fu, Punit Singh Koura, Ruan Silva, Sheng Shen, Emily Dinan, Suchin Gururangan, and Mike Lewis. 2025. BTS: Harmonizing Specialized Experts into a Generalist LLM. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6827–6845, Suzhou, China. Association for Computational Linguistics.