@inproceedings{liu-etal-2025-divide,
title = "Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for {LLM} Agents",
author = "Liu, Jiale and
Zeng, Yifan and
Zhang, Shaokun and
Zhang, Chi and
H{\o}jmark-Bertelsen, Malte and
Gadeberg, Marie Normann and
Wang, Huazheng and
Wu, Qingyun",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1034/",
pages = "18990--19012",
ISBN = "979-8-89176-335-7",
abstract = "LLM-based optimization has shown remarkable potential in improving agentic systems. However, the conventional approach of prompting LLM-based generative optimizer with the trajectories on the whole training dataset in a single pass becomes untenable as datasets grow, leading to context window overflow and degraded pattern recognition. To address these challenges, we propose Fine-grained Generative Optimization (FGO), a scalable framework that divides large optimization tasks into manageable subsets, performs targeted optimizations, and systematically combines optimized components through progressive merging.Evaluation across ALFWorld, LogisticsQA, and GAIA benchmarks demonstrates that FGO outperforms conventional approach by 1.6-8.6{\%} while reducing average prompt token consumption by 56.3{\%}. Our framework provides a practical solution for scaling up LLM-based generative optimization of increasingly sophisticated agentic systems. Further analysis demonstrates that FGO achieves the most consistent performance gain in all training dataset sizes, showcasing its scalability and efficiency."
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<abstract>LLM-based optimization has shown remarkable potential in improving agentic systems. However, the conventional approach of prompting LLM-based generative optimizer with the trajectories on the whole training dataset in a single pass becomes untenable as datasets grow, leading to context window overflow and degraded pattern recognition. To address these challenges, we propose Fine-grained Generative Optimization (FGO), a scalable framework that divides large optimization tasks into manageable subsets, performs targeted optimizations, and systematically combines optimized components through progressive merging.Evaluation across ALFWorld, LogisticsQA, and GAIA benchmarks demonstrates that FGO outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. Our framework provides a practical solution for scaling up LLM-based generative optimization of increasingly sophisticated agentic systems. Further analysis demonstrates that FGO achieves the most consistent performance gain in all training dataset sizes, showcasing its scalability and efficiency.</abstract>
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%0 Conference Proceedings
%T Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents
%A Liu, Jiale
%A Zeng, Yifan
%A Zhang, Shaokun
%A Zhang, Chi
%A Højmark-Bertelsen, Malte
%A Gadeberg, Marie Normann
%A Wang, Huazheng
%A Wu, Qingyun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-divide
%X LLM-based optimization has shown remarkable potential in improving agentic systems. However, the conventional approach of prompting LLM-based generative optimizer with the trajectories on the whole training dataset in a single pass becomes untenable as datasets grow, leading to context window overflow and degraded pattern recognition. To address these challenges, we propose Fine-grained Generative Optimization (FGO), a scalable framework that divides large optimization tasks into manageable subsets, performs targeted optimizations, and systematically combines optimized components through progressive merging.Evaluation across ALFWorld, LogisticsQA, and GAIA benchmarks demonstrates that FGO outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. Our framework provides a practical solution for scaling up LLM-based generative optimization of increasingly sophisticated agentic systems. Further analysis demonstrates that FGO achieves the most consistent performance gain in all training dataset sizes, showcasing its scalability and efficiency.
%U https://aclanthology.org/2025.findings-emnlp.1034/
%P 18990-19012
Markdown (Informal)
[Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents](https://aclanthology.org/2025.findings-emnlp.1034/) (Liu et al., Findings 2025)
ACL
- Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, and Qingyun Wu. 2025. Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 18990–19012, Suzhou, China. Association for Computational Linguistics.