学会配置智能体人工智能系统

摘要

基于大语言模型的智能体系统配置涉及从庞大的组合设计空间中选择工作流、工具、令牌预算和提示策略，目前通常采用固定的大型模板或人工调优的启发式方法。这种配置方式会导致系统行为脆弱且产生不必要的计算开销，因为无论输入查询难易程度如何，往往都采用相同的繁琐配置。我们将智能体配置定义为按查询决策的问题，并提出了ARC（智能体资源与配置学习器），该系统通过强化学习训练轻量级分层策略，动态调整配置方案。在涵盖推理任务和工具增强问答的多个基准测试中，学习得到的策略持续优于人工设计及其他基线方法，任务准确率最高提升25%，同时降低了令牌消耗和运行时间成本。这些结果表明，针对每个查询学习智能体配置是替代"一刀切"设计理念的有效方案。

English

Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries. We formulate agent configuration as a query-wise decision problem and introduce ARC (Agentic Resource & Configuration learner), which learns a light-weight hierarchical policy using reinforcement learning to dynamically tailor these configurations. Across multiple benchmarks spanning reasoning and tool-augmented question answering, the learned policy consistently outperforms strong hand-designed and other baselines, achieving up to 25% higher task accuracy while also reducing token and runtime costs. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.

学会配置智能体人工智能系统

Learning to Configure Agentic AI Systems

摘要

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