SkillOrchestra：基於技能遷移的智能體路由學習框架

摘要

複合式人工智慧系統展現出超越單一模型的潛力，但其成功關鍵在於有效的協調機制。現有的路由方法面臨兩大局限：(1) 輸入級路由器僅能進行粗粒度的查詢級決策，無法適應動態變化的任務需求；(2) 基於強化學習的協調器調整成本高昂，且在多輪對話場景中容易出現「路由崩塌」現象，反覆呼叫單一強大但成本昂貴的選項。我們提出SkillOrchestra——一個具備技能感知能力的協調框架。該框架不直接進行端到端的路由策略學習，而是從執行經驗中提取細粒度技能，並在此基礎上建模各代理在特定技能領域的能力與成本。部署時，協調器會即時推斷當前互動所需的技能需求，並在明確的性能-成本權衡下選擇最適合的代理。在十個基準測試上的大規模實驗表明，SkillOrchestra相較於最先進的基於強化學習的協調器性能提升達22.5%，且學習成本分別比Router-R1和ToolOrchestra降低700倍與300倍。這些結果證明，顯式技能建模能實現可擴展、可解釋且樣本高效的協調機制，為數據密集的強化學習方法提供了理論嚴謹的替代方案。程式碼已開源於：https://github.com/jiayuww/SkillOrchestra。

English

Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input-level routers make coarse query-level decisions that ignore evolving task requirements; (2) RL-trained orchestrators are expensive to adapt and often suffer from routing collapse, repeatedly invoking one strong but costly option in multi-turn scenarios. We introduce SkillOrchestra, a framework for skill-aware orchestration. Instead of directly learning a routing policy end-to-end, SkillOrchestra learns fine-grained skills from execution experience and models agent-specific competence and cost under those skills. At deployment, the orchestrator infers the skill demands of the current interaction and selects agents that best satisfy them under an explicit performance-cost trade-off. Extensive experiments across ten benchmarks demonstrate that SkillOrchestra outperforms SoTA RL-based orchestrators by up to 22.5% with 700x and 300x learning cost reduction compared to Router-R1 and ToolOrchestra, respectively. These results show that explicit skill modeling enables scalable, interpretable, and sample-efficient orchestration, offering a principled alternative to data-intensive RL-based approaches. The code is available at: https://github.com/jiayuww/SkillOrchestra.

SkillOrchestra：基於技能遷移的智能體路由學習框架

SkillOrchestra: Learning to Route Agents via Skill Transfer

摘要

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