Autonomous agents powered by large language models (LLMs) show significant potential for achieving high autonomy in various scenarios such as software development. Recent research has shown that LLM agents can leverage past experiences to reduce errors and enhance efficiency. However, the static experience paradigm, reliant on a fixed collection of past experiences acquired heuristically, lacks iterative refinement and thus hampers agents' adaptability. In this paper, we introduce the Iterative Experience Refinement framework, enabling LLM agents to refine experiences iteratively during task execution. We propose two fundamental patterns: the successive pattern, refining based on nearest experiences within a task batch, and the cumulative pattern, acquiring experiences across all previous task batches. Augmented with our heuristic experience elimination, the method prioritizes high-quality and frequently-used experiences, effectively managing the experience space and enhancing efficiency. Extensive experiments show that while the successive pattern may yield superior results, the cumulative pattern provides more stable performance. Moreover, experience elimination facilitates achieving better performance using just 11.54% of a high-quality subset.

在本文中，我们介绍了迭代式经验优化框架，使得大型语言模型（LLMs）驱动的自主代理能够在任务执行过程中迭代优化经验。我们提出了两种基本模式：连续模式，基于任务批次内最近的经验进行优化，和累积模式，跨越所有先前任务批次进行经验积累。我们的方法结合了启发式经验消除，有效管理经验空间并提高效率。广泛的实验表明，连续模式可能会产生更好的结果，而累积模式提供更稳定的性能。此外，经验消除助于仅使用高质量子集的11.54%实现更好的性能。

软件开发智能体的迭代经验优化