Recent studies show that LLMs possess different skills and specialize in different tasks. In fact, we observe that their varied performance occur in several levels of granularity. For example, in the code optimization task, code LLMs excel at different optimization categories and no one dominates others. This observation prompts the question of how one leverages multiple LLM agents to solve a coding problem without knowing their complementary strengths a priori. We argue that a team of agents can learn from each other's successes and failures so as to improve their own performance. Thus, a lesson is the knowledge produced by an agent and passed on to other agents in the collective solution process. We propose a lesson-based collaboration framework, design the lesson solicitation--banking--selection mechanism, and demonstrate that a team of small LLMs with lessons learned can outperform a much larger LLM and other multi-LLM collaboration methods.

翻译：近期研究表明，大语言模型（LLM）具备不同的技能且在不同任务上各有所长。事实上，我们观察到其性能差异体现在多个粒度层级。例如在代码优化任务中，不同代码LLM在不同优化类别上表现优异，且没有单一模型能全面占优。这一观察引出了关键问题：如何在未知智能体互补优势的前提下，利用多个LLM智能体协同解决编码问题？我们认为，智能体团队能够通过彼此的成功与失败经验进行相互学习，从而提升整体性能。因此，“经验”被定义为智能体在集体求解过程中生成并传递给其他智能体的知识。我们提出基于经验学习的协作框架，设计了经验征集-存储-选择机制，并证明通过经验学习的小型LLM团队能够超越规模更大的单一LLM及其他多LLM协作方法。