arXiv AI June 30, 2026 By Runze Zhao, Dongruo Zhou, Sumit Kumar Jha, Nathaniel D. Bastian, Ankit Shah

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

arXiv:2606. 29126v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vectors detached from the structure of the observations they summarize.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 2

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

arXiv:2605. 18077v2 Announce Type: replace Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information.

By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 23

Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

arXiv:2607. 19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability.

By Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi

agents reinforcement-learning

arXiv Machine Learning

Jul 1

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.

By Tommaso Marzi, Cesare Alippi, Andrea Cini

agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

arXiv:2606. 10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering.

By Jaewan Park, Solbee Cho, Jay-Yoon Lee

llms agents nlp fine-tuning benchmarks

arXiv AI

Jul 28

Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus

arXiv:2604. 13472v2 Announce Type: replace-cross Abstract: Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple interacting agents.

By Zijian Zhao, Jing Gao, Sen Li

llms agents diffusion reinforcement-learning benchmarks

arXiv AI

Jun 29

LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

arXiv:2606. 28182v1 Announce Type: cross Abstract: Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years.

By Qinhong Zhou, Chuang Gan, Anoop Cherian

llms agents robotics fine-tuning benchmarks

Related stories

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus

LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior