Reward Modeling for Multi-Agent Orchestration
Multi-Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by limited supervision and high computational cost. We propose Orchestration Reward Modeling (OrchRM), a self-supervised framework for evaluating orchestration quality without human annotations. OrchRM leverages intermediate artifacts from multi-agent executions to construct win-lose pairs for Bradley-Terry reward model training. Unlike existing MAS test-time scaling and orchestrator training frameworks that rely on costly sub-agent rollouts, OrchRM operates directly at the orchestration level, enabling efficient and ...
| Authors | King Yeung Tsang, Zihao Zhao, Vishal Venkataramani et al. |
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| Primary category | cs.AI |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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