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[2503.12434] A Survey on the Optimization of Large Language Model-based Agents
Summary
This survey reviews optimization techniques for Large Language Model (LLM)-based agents, categorizing methods into parameter-driven and parameter-free approaches, and discusses their applications and challenges.
Why It Matters
As LLMs become integral to autonomous systems, understanding their optimization is crucial for enhancing performance in complex environments. This survey fills a gap by systematically comparing existing strategies, guiding researchers and practitioners in improving LLM-based agents.
Key Takeaways
- The survey categorizes optimization methods into parameter-driven and parameter-free approaches.
- Parameter-driven methods include fine-tuning and reinforcement learning, focusing on enhancing agent capabilities.
- Parameter-free strategies utilize prompt engineering and knowledge retrieval to optimize behavior.
- The paper discusses datasets and benchmarks critical for evaluating LLM-based agents.
- Identifies key challenges and future directions for research in LLM optimization.
Computer Science > Artificial Intelligence arXiv:2503.12434 (cs) [Submitted on 16 Mar 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:A Survey on the Optimization of Large Language Model-based Agents Authors:Shangheng Du, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xin Jiang, Yanhong Bai, Liang He View a PDF of the paper titled A Survey on the Optimization of Large Language Model-based Agents, by Shangheng Du and 6 other authors View PDF HTML (experimental) Abstract:With the rapid development of Large Language Models (LLMs), LLM-based agents have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks. However, current work typically relies on prompt design or fine-tuning strategies applied to vanilla LLMs, which often leads to limited effectiveness or suboptimal performance in complex agent-related environments. Although LLM optimization techniques can improve model performance across many general tasks, they lack specialized optimization towards critical agent functionalities such as long-term planning, dynamic environmental interaction, and complex decision-making. Although numerous recent studies have explored various strategies to optimize LLM-based agents for complex agent tasks, a systematic review summarizing and comparing these methods from a holistic perspective is still lacking. In this survey, we provide a comprehensive review of LLM-based agent optimization approaches, categorizing them into parame...