Summary of Iterative Experience Refinement Of Software-developing Agents, by Chen Qian et al.
Iterative Experience Refinement of Software-Developing Agents
by Chen Qian, Jiahao Li, Yufan Dang, Wei Liu, YiFei Wang, Zihao Xie, Weize Chen, Cheng Yang, Yingli Zhang, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative framework for large language model (LLM) agents to achieve higher autonomy in software development and other scenarios. The authors address the limitations of traditional static experience paradigms by introducing Iterative Experience Refinement, which enables LLM agents to refine their experiences iteratively during task execution. Two fundamental patterns are proposed: successive refinement based on nearest experiences within a task batch, and cumulative refinement across all previous task batches. To manage the experience space effectively, the authors also introduce heuristic experience elimination, prioritizing high-quality and frequently-used experiences. Experimental results show that while the successive pattern yields superior results, the cumulative pattern provides more stable performance. Moreover, experience elimination enables better performance using just 11.54% of a high-quality subset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language model agents can help us with tasks like software development by remembering what they learned before. These agents are getting smarter and more efficient because they can use their past experiences to make fewer mistakes. But there’s a problem – these experiences don’t get updated or refined as the agent works on new tasks. This makes it hard for the agent to adapt to changes and learn from its mistakes. The authors of this paper came up with a solution called Iterative Experience Refinement. It lets the agent refine its experiences as it goes, making it smarter and more efficient over time. Two ways they do this are by refining based on what happened recently or across all previous tasks. They also found that getting rid of old, not-so-useful experiences helps the agent work better with a smaller set of good experiences. |
Keywords
» Artificial intelligence » Large language model
