Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
Karen Liu, and Dorsa Sadigh
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Centralized-critic actor-critic training (CoLLM-CC) improves sample efficiency and stability over Monte-Carlo multi-agent RL for training decentralized LLM collaboration.
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Bayesian Social Deduction with Graph-Informed Language Models
Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
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Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
Centralized-critic actor-critic training (CoLLM-CC) improves sample efficiency and stability over Monte-Carlo multi-agent RL for training decentralized LLM collaboration.