The field of large language models is moving towards more efficient optimization techniques to improve training speed and scalability. Recent developments focus on enhancing the robustness and efficiency of distributed training, with a emphasis on minimizing computational and memory overhead. Notable advancements include the application of Nesterov momentum to pseudo-gradients, fault-tolerant optimization methods, and block-periodic orthogonalization techniques. These innovations have led to significant improvements in training speed and resilience, making them promising solutions for large-scale language model training. Noteworthy papers include: SNOO, which achieves compute factor gains of 1.5-2.5x in a non-distributed setting by applying Nesterov momentum to pseudo-gradients. MeCeFO, which ensures robust training with minimal overhead by leveraging skip-connection, recomputation, and low-rank gradient approximation. MuonBP, which applies orthogonalization independently to matrix shards on each device and periodically performs full orthogonalization to maintain training stability at scale. Unbiased Gradient Low-Rank Projection, which investigates the layerwise sampling technique for debiasing low-rank projection mechanisms and provides a novel and unbiased low-rank optimization method. AsyncHZP, which proposes a novel asynchronous variant of ZeRO designed to achieve superior performance while maintaining simplicity and memory efficiency. Collective Communication for 100k+ GPUs, which presents the NCCLX collective communication framework, engineered to optimize performance across the full LLM lifecycle.