Belief-Conditioned Multi-Agent Actor Critic for Decentralized LLM Collaboration

Tianle Chen; Shuo Liu; Christopher Amato

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arXiv · 2610.35398

Belief-Conditioned Multi-Agent Actor Critic for Decentralized LLM Collaboration

Tianle Chen·Shuo Liu·Christopher Amato

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Abstract

Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL), but many approaches still rely on predefined workflows, limiting efficiency and scalability. Decentralized LLM collaboration is challenging because agents execute independently with limited communication, share a joint objective, and must infer useful information from communication traces. In this paper, we propose BCMAAC, a Belief-Conditioned Multi-Agent Actor-Critic method that maintains beliefs over contextual information and uses them to facilitate training. Experiments on Minecraft game-playing, web searching and tool-using tasks show that BCMAAC improves task performance and collaborative behavior compared to naive actor-critic baselines. The improvements are particularly pronounced in multi-turn tasks with richer contextual information, where belief modeling provides a clear advantage.

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BibTeX RIS

Tianle Chen, Shuo Liu, Christopher Amato. 2026-10-26. Belief-Conditioned Multi-Agent Actor Critic for Decentralized LLM Collaboration. https://arxiv.org/abs/2610.35398

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