AdaptFlow: Adaptive Workflow Optimization via Meta-Learning

ACL ARR 2025 May Submission428 Authors

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.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: LLM/AI agents

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English

Keywords: LLM, agent, agentic workflow

Submission Number: 428