Evolving Multi-Agent Orchestration
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OpenBMB researchers propose a “puppeteer-style” paradigm for multi-agent LLM collaboration, where a centralized orchestrator dynamically directs agents based on evolving task states. Trained via reinforcement learning, the system achieves superior performance with reduced computational costs across math, knowledge, and software development tasks.
Dynamic orchestration: A centralized policy selects which agent to activate at each reasoning step, treating multi-agent collaboration as a sequential decision process. This decouples agent selection from internal behaviors, enabling flexible coordination without extensive retraining.
Adaptive evolution via RL: The orchestrator uses REINFORCE to learn from completed tasks, progressively pruning less effective agents and favoring compact reasoning chains. A reward function balances solution quality with computational efficiency through a tunable weighting factor.
Emergent topology patterns: As training progresses, the system develops compact, cyclic reasoning structures rather than static chains or trees. Graph density increases, and communication concentrates among “hub” agents, enabling recursive critique and continual refinement.
Strong benchmark results: Puppeteer outperforms baselines including AFlow, MacNet, and EvoAgent across GSM-Hard, MMLU-Pro, SRDD, and CommonGen-Hard. The evolved system achieves 0.77 average accuracy in the Titan (large model) setting while reducing token consumption.
Efficiency without sacrifice: Unlike prior multi-agent systems that trade efficiency for performance, Puppeteer reduces both token usage and active agent count over training. In Titan settings, agents learn to terminate reasoning earlier; in Mimas (smaller model) settings, the system selects lower-cost agents while maintaining chain length.
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