arXiv:2606.13598v1 Announce Type: cross Abstract: 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 or
The rapid development and deployment of LLMs necessitate more efficient and scalable methods for training multi-agent systems, moving beyond labor-intensive human supervision.
This development addresses a key bottleneck in scaling AI agent systems by enabling self-supervised training, which is crucial for building more autonomous and complex AI applications.
The reliance on human annotations for evaluating multi-agent orchestration is significantly reduced, potentially accelerating the development and deployment cycles of AI agents.
- · AI Agent developers
- · Companies adopting multi-agent systems
- · Researchers in reinforcement learning
- · Platforms reliant on manual AI agent evaluation
- · Traditional human-in-the-loop annotation services
More sophisticated and autonomous multi-agent AI systems become feasible due to scalable training methods.
The proliferation of highly coordinated AI agents could begin to automate more complex professional tasks and workflows.
Increased efficiency in AI agent development could lead to broader societal integration of AI, impacting labor markets and economic structures at an accelerated pace.
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Read at arXiv cs.CL