@inproceedings{zhu-etal-2025-adaptflow,
title = "{A}dapt{F}low: Adaptive Workflow Optimization via Meta-Learning",
author = "Zhu, Runchuan and
Jiang, Bowen and
Mei, Lingrui and
Yang, Fangkai and
Wang, Lu and
Gao, Haoxiang and
Bai, Fengshuo and
Zhao, Pu and
Lin, Qingwei and
Rajmohan, Saravan and
Zhang, Dongmei",
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.175/",
pages = "3287--3302",
ISBN = "979-8-89176-335-7",
abstract = "Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows{---}structured sequences of LLM invocations designed to solve complex tasks. However, existing approaches often rely on static templates or manually designed workflows, which limit adaptability to diverse tasks and hinder scalability. We propose AdaptFlow, a natural language-based meta-learning framework inspired by model-agnostic meta-learning (MAML). AdaptFlow uses a bi-level optimization process: the inner loop performs task-specific adaptation via LLM-generated feedback, while the outer loop consolidates these refinements into a shared, generalizable initialization. Evaluated across question answering, code generation, and mathematical reasoning benchmarks, AdaptFlow consistently outperforms both manually crafted and automatically searched baselines, achieving state-of-the-art results with strong generalization across tasks and models."
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<abstract>Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows—structured sequences of LLM invocations designed to solve complex tasks. However, existing approaches often rely on static templates or manually designed workflows, which limit adaptability to diverse tasks and hinder scalability. We propose AdaptFlow, a natural language-based meta-learning framework inspired by model-agnostic meta-learning (MAML). AdaptFlow uses a bi-level optimization process: the inner loop performs task-specific adaptation via LLM-generated feedback, while the outer loop consolidates these refinements into a shared, generalizable initialization. Evaluated across question answering, code generation, and mathematical reasoning benchmarks, AdaptFlow consistently outperforms both manually crafted and automatically searched baselines, achieving state-of-the-art results with strong generalization across tasks and models.</abstract>
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%0 Conference Proceedings
%T AdaptFlow: Adaptive Workflow Optimization via Meta-Learning
%A Zhu, Runchuan
%A Jiang, Bowen
%A Mei, Lingrui
%A Yang, Fangkai
%A Wang, Lu
%A Gao, Haoxiang
%A Bai, Fengshuo
%A Zhao, Pu
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%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 zhu-etal-2025-adaptflow
%X Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows—structured sequences of LLM invocations designed to solve complex tasks. However, existing approaches often rely on static templates or manually designed workflows, which limit adaptability to diverse tasks and hinder scalability. We propose AdaptFlow, a natural language-based meta-learning framework inspired by model-agnostic meta-learning (MAML). AdaptFlow uses a bi-level optimization process: the inner loop performs task-specific adaptation via LLM-generated feedback, while the outer loop consolidates these refinements into a shared, generalizable initialization. Evaluated across question answering, code generation, and mathematical reasoning benchmarks, AdaptFlow consistently outperforms both manually crafted and automatically searched baselines, achieving state-of-the-art results with strong generalization across tasks and models.
%U https://aclanthology.org/2025.findings-emnlp.175/
%P 3287-3302
Markdown (Informal)
[AdaptFlow: Adaptive Workflow Optimization via Meta-Learning](https://aclanthology.org/2025.findings-emnlp.175/) (Zhu et al., Findings 2025)
ACL
- Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2025. AdaptFlow: Adaptive Workflow Optimization via Meta-Learning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 3287–3302, Suzhou, China. Association for Computational Linguistics.