标题： 通过强化学习控制集中式多智能体LLM系统的性能和预算 显示英文标题

标题： Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning

摘要： 大型语言模型（LLMs）在不同领域表现出互补的优势，并且具有不同的推理成本，这促使设计多智能体LLM系统，其中专门模型可以高效协作。 现有方法主要依赖于去中心化框架，对于每个输入都会调用多个LLM，从而导致显著且不可控的推理成本。 在本工作中，我们引入了一个中心化的多LLM框架，其中控制器LLM以成本高效且可控的方式选择性地协调专家模型的集合。 我们将这种协调问题形式化为具有双重目标的强化学习：最大化任务性能同时最小化总体推理成本。 此外，我们期望多智能体系统在推理过程中能够根据不同的预算条件表现出适应性行为。 为此，我们提出了CoRL，这是一个在可控多预算设置中优化性能与成本权衡的强化学习框架。 在四个多样化的基准测试中进行的实验表明，CoRL使单一系统在高预算设置下超越最佳专家LLM，同时在更经济的低预算模式中保持强大的性能，突显了中心化协调在可扩展和成本高效的多智能体LLM系统中的有效性。 显示英文摘要

摘要： Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialized models collaborate efficiently. Existing approaches predominantly rely on decentralized frameworks, which invoke multiple LLMs for every input and thus lead to substantial and uncontrolled inference costs. In this work, we introduce a centralized multi-LLM framework, where a controller LLM selectively coordinates a pool of expert models in a cost-efficient and cost-controllable manner. We formulate this coordination problem as reinforcement learning with dual objectives: maximizing task performance while minimizing the overall inference cost. In addition, we expect the multi-agent system to have adapted behavior with different budget conditions during inference. To this end, we propose CoRL, a reinforcement learning framework that optimizes the performance cost trade-off in a controllable multi-budget setting. Experiments on four diverse benchmarks demonstrate that CoRL enables a single system to surpass the best expert LLM under high-budget settings, while maintaining strong performance in more economical low-budget modes, highlighting the effectiveness of centralized coordination for scalable and cost-efficient multi-agent LLM systems.