TOA uses Monte Carlo Tree Search to dynamically orchestrate multiple LLMs during best-of-N sampling, and the authors report compute-efficiency gains over single-agent and fixed-workflow baselines on alignment, translation, and math.
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Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration
TOA uses Monte Carlo Tree Search to dynamically orchestrate multiple LLMs during best-of-N sampling, and the authors report compute-efficiency gains over single-agent and fixed-workflow baselines on alignment, translation, and math.