As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery essential. A stalled training rank can trigger an NVIDIA Collective Communications Library (NCCL) timeout, a GPU node can stop responding to the scheduler, or a straggler can slow the entire job. Each disruption is manageable on its own, but the cumulative impact and recovery overhead determine how efficiently the system converts wall-clock time into productive training progress or goodput.