The field of multi-agent systems and reinforcement learning is moving towards more scalable and efficient solutions. Researchers are exploring new frameworks and methods to improve the safety and performance of multi-agent control, such as decentralized physics-informed machine learning and adversarial reinforcement learning. These approaches have shown promising results in various applications, including multi-agent navigation and pursuit-evasion scenarios. Noteworthy papers include: MAD-PINN, which achieves superior safety-performance trade-offs in multi-agent navigation tasks, and A Framework for Scalable Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab, which enables efficient training and evaluation of adversarial policies in high-fidelity physics simulations. Additionally, Octax provides a high-performance suite of classic arcade game environments for reinforcement learning, offering significant improvements in training speed and scalability.