Title: DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making

Source: http://arxiv.org/abs/2604.23557v1

Summary:

This paper proposes a unified framework that treats multi-agent decision-making as a dialogue-style sequence prediction problem, enabling robust zero-shot generalization across heterogeneous environments. It establishes a foundational approach for scaling multi-agent reinforcement learning by leveraging the flexible architectural interface of large language models.