FineWeb

q0: Hyper-Epoch Pretraining turns multi-epoch budgets into diverse model populations for better generalization

A new arXiv preprint introduces hyper-epoch pretraining (q0), a framework that reframes multi-epoch training as exploration of a model population rather than refinement of a single model. The approach uses three primitives—cyclic schedules with anti-correlated learning rate and weight decay, chain distillation, and a learned prior for inference-time weighting—to achieve lower validation loss than single-model training. On a 1.8B-parameter model trained on FineWeb, q0 matches a 256-epoch ensemble baseline using only ~56 epochs (~4.6× fewer), with cumulative ~12.9× data efficiency under the Slowrun setting. The work directly addresses the emerging regime where compute scales faster than high-quality data supply.