most citedBeyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks

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cs.AI2025

Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning

Pei Yang, Ke Zhang, Ji Wang +5

We present CRM (Multi-Agent Collaborative Reward Model), a framework that replaces a single black-box reward model with a coordinated team of specialist evaluators to improve robus…

cs.AI2025★ 1 cited

Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks

Aaron Xuxiang Tian, Ruofan Zhang, Jiayao Tang +12

We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until…