Training algorithms stop needing a tuning sweep: on AlgoPerf v1's self-tuning track, Schedule-Free AdamW wins 13 submissions with one configuration
On AlgoPerf v1's self-tuning track, where 13 submissions each had to run all nine workloads with a single fixed hyperparameter configuration, Schedule-Free AdamW v2 (PyTorch) ranked first on wall-clock time-to-target, indicating that schedule-free iterate averaging generalizes across heterogeneous architectures without per-task tuning.