@inproceedings{cho-etal-2026-supplement,
title = "Supplement Generation Training for Enhancing Agentic Task Performance",
author = "Cho, Young Min and
Bonadiman, Daniele and
Bhargavi, Divya and
Alkhouli, Tamer and
Romeo, Salvatore and
Jiang, Dongwei and
Pahwa, Khushbu and
Ge, Yubin and
Ishii, Etsuko and
Sunkara, Monica and
Zhang, Yi",
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.2021/",
pages = "40662--40675",
ISBN = "979-8-89176-395-1",
abstract = "Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are continuously released. Instead of post-training massive models for every new task or domain, we propose Supplement Generation Training (SGT), a more efficient and sustainable strategy. SGT trains a smaller LLM to generate useful supplemental text that, when appended to the original input, helps the larger LLM solve the task more effectively. These lightweight models can dynamically adapt supplements to task requirements, improving performance without modifying the underlying large models. This approach decouples task-specific optimization from large foundation models and enables more flexible, cost-effective deployment of LLM-powered agents in real-world applications."
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<abstract>Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are continuously released. Instead of post-training massive models for every new task or domain, we propose Supplement Generation Training (SGT), a more efficient and sustainable strategy. SGT trains a smaller LLM to generate useful supplemental text that, when appended to the original input, helps the larger LLM solve the task more effectively. These lightweight models can dynamically adapt supplements to task requirements, improving performance without modifying the underlying large models. This approach decouples task-specific optimization from large foundation models and enables more flexible, cost-effective deployment of LLM-powered agents in real-world applications.</abstract>
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%0 Conference Proceedings
%T Supplement Generation Training for Enhancing Agentic Task Performance
%A Cho, Young Min
%A Bonadiman, Daniele
%A Bhargavi, Divya
%A Alkhouli, Tamer
%A Romeo, Salvatore
%A Jiang, Dongwei
%A Pahwa, Khushbu
%A Ge, Yubin
%A Ishii, Etsuko
%A Sunkara, Monica
%A Zhang, Yi
%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 cho-etal-2026-supplement
%X Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are continuously released. Instead of post-training massive models for every new task or domain, we propose Supplement Generation Training (SGT), a more efficient and sustainable strategy. SGT trains a smaller LLM to generate useful supplemental text that, when appended to the original input, helps the larger LLM solve the task more effectively. These lightweight models can dynamically adapt supplements to task requirements, improving performance without modifying the underlying large models. This approach decouples task-specific optimization from large foundation models and enables more flexible, cost-effective deployment of LLM-powered agents in real-world applications.
%U https://aclanthology.org/2026.findings-acl.2021/
%P 40662-40675
Markdown (Informal)
[Supplement Generation Training for Enhancing Agentic Task Performance](https://aclanthology.org/2026.findings-acl.2021/) (Cho et al., Findings 2026)
ACL
- Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, and Yi Zhang. 2026. Supplement Generation Training for Enhancing Agentic Task Performance. In Findings of the Association for Computational Linguistics: ACL 2026, pages 40662–40675, San Diego, California, United States. Association for Computational Linguistics.