Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
Summary: arXiv:2604.18612v1 Announce Type: cross
Large Language Models (LLMs) have become increasingly adept at tackling complex reasoning tasks, showcasing remarkable capabilities that have been further enhanced by innovative prompting strategies such as Chain-of-Thought (CoT). These advancements, however, often hinge on the use of manually designed static prompts, which can introduce significant variability in performance depending on decoding configurations and task distributions. This inconsistency can hinder the overall effectiveness and transferability of LLMs across different applications.
Current methods for automatic prompt optimization primarily rely on single-agent local search techniques. These approaches tend to overlook the potential benefits of simultaneously optimizing both prompts and decoding hyperparameters within a cohesive framework. As a result, the quest for stable global improvements in reasoning capabilities remains largely unaddressed.
To overcome these limitations, we introduce Agent-GWO, a dynamic prompt optimization framework specifically designed for enhancing complex reasoning in LLMs. This innovative approach integrates prompt templates and decoding hyperparameters as inheritable configurations for collaborative agents. By employing the leader-follower mechanism of the Grey Wolf Optimizer (GWO), Agent-GWO enables the automatic selection of three leader agents—denoted as $\alpha$, $\beta$, and $\delta$—which guide the collaborative updates of the remaining agents.
The iterative nature of this process fosters convergence toward robust optimal reasoning configurations, which can be seamlessly integrated into LLMs for inference tasks. The results of extensive experiments conducted on a variety of mathematical and hybrid reasoning benchmarks across different LLM architectures demonstrate that Agent-GWO consistently outperforms existing methods of prompt optimization in terms of both accuracy and stability.
Key Features of Agent-GWO
- Dynamic Optimization: Agent-GWO adapts to varying task distributions and decoding configurations, minimizing performance fluctuations.
- Collaborative Agent Framework: By leveraging a multi-agent system, Agent-GWO facilitates simultaneous updates, improving overall reasoning capabilities.
- Leader-Follower Mechanism: The selection of leader agents ($\alpha$, $\beta$, and $\delta$) optimizes the search process and accelerates convergence to optimal configurations.
- Extensive Benchmarking: The framework has been rigorously tested across diverse reasoning tasks, showcasing its superior performance compared to conventional methods.
- Public Code Release: The code for Agent-GWO will be made publicly available, encouraging further research and development in this area.
The introduction of Agent-GWO marks a significant step forward in the quest for more effective and adaptable prompt optimization techniques in LLMs. By addressing the inherent challenges associated with static prompts and single-agent optimization, this framework offers a promising avenue for enhancing the reasoning capabilities of AI systems across various applications. As research in this domain continues to evolve, Agent-GWO is poised to play a crucial role in advancing the state of the art in large language modeling.