Iterative Experience Refinement of Software-Developing Agents

arXiv:2405.04219v1 Announce Type: new
Abstract: 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 …

abstract agents arxiv autonomous autonomous agents autonomy collection cs.ai cs.cl cs.ma cs.se development efficiency errors experience however iterative language language models large language large language models llm llms paradigm reduce research show software software development type