The field of distributed machine learning is moving towards scalable and efficient training methods, with a focus on improving communication and reducing latency. Researchers are exploring new architectures and techniques to overcome the challenges of large-scale distributed training, such as performance variability, congestion control, and reliable connectivity. Notable advancements include the development of probabilistic performance modeling frameworks, unified congestion control systems, and innovative optical interconnects. These innovations have the potential to significantly improve training times, reduce communication overhead, and enable the scaling of complex models. Noteworthy papers include: PRISM, which presents a performance modeling framework for large-scale distributed training, demonstrating up to 1.26x performance improvement potential. FlexLink, which introduces a collective communication framework that aggregates heterogeneous links to improve bandwidth by up to 27%. Accelerating Frontier MoE Training with 3D Integrated Optics, which explores the design tradeoffs of scale-up technologies and demonstrates an 8X increase in scale-up capability and a 2.7X reduction in time-to-train. Reimagining RDMA Through the Lens of ML, which proposes a domain-specific RDMA transport that reduces 99th-percentile latency by up to 2.3x. RailS, which presents a distributed load-balancing framework that minimizes all-to-all completion time in MoE training, improving bus bandwidth by 20%-78% and reducing completion time by 17%-78%. HybridEP, which proposes a modeling-guided framework to optimize Expert Parallelism under constrained bandwidth, achieving up to 5.6x speedup over existing state-of-the-art MoE training systems.