Definition of Large-scale Asynchronous Training Large-scale asynchronous training is a machine-learning training approach in which a large amount of computational work is distributed across many workers, devices, machines, or training processes that can perform and contribute training operations without requiring every participant to remain synchronized at each step. In conventional tightly synchronized training, participating workers commonly perform computation and then wait at coordination points so that their results can be aggregated before the next shared training step proceeds. In an asynchronous approach, different workers may instead generate training data, compute updates, evaluate model behavior, process experience, or perform other parts of the training workflow at different rates, while their outputs are collected and incorporated according to the design of the training system. This can be particularly valuable for large and heterogeneous training workloads—including reinforcement learning, agentic training, distributed model optimization, and workloads involving environments or tools with variable response times—because faster workers do not necessarily have to remain idle while waiting for slower ones. At large scale, asynchronous training can therefore improve computational utilization, throughput, scalability, and tolerance of variable workloads, although it also introduces challenges such as stale training information, uneven worker progress, coordination complexity, inconsistent model versions, and the need to determine how asynchronously generated data or updates should influence the continuously evolving model. ...