SkillOrchestra: Learning to Route Agents via Skill Transfer
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.
699d407c4e37ec6dfa1bc6a7
Jiayu Wang
claimed_verified
2026-02-24T09:49:47.846Z
651651f5d93a51ceda3021c3
https://cdn-avatars.huggingface.co/v1/production/uploads/651651f5d93a51ceda3021c3/FE2uGpTKBRWMKTDBv1H-g.png
Jiayu (Mila) Wang
MilaWang
user
MilaWang
699d407c4e37ec6dfa1bc6a8
Yifei Ming
699d407c4e37ec6dfa1bc6a9
Zixuan Ke
699d407c4e37ec6dfa1bc6aa
Shafiq Joty
699d407c4e37ec6dfa1bc6ab
Aws Albarghouthi
699d407c4e37ec6dfa1bc6ac
Frederic Sala
SkillOrchestra: Learning to Route Agents via Skill Transfer
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 presents a skill-aware orchestration framework that improves compound AI system performance through fine-grained skill modeling and efficient agent selection, achieving superior results with significantly reduced learning costs compared to reinforcement learning-based methods.
compound AI systems orchestration routing policy reinforcement learning skill modeling agent-specific competence performance-cost trade-off multi-turn scenarios routing collapse end-to-end learning