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stat.ML2026
On the Stability and Convergence of Flow Map Training
Ishan Nath, Tom Stanic, Avishek Joey Bose +2
Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation. While progress has been made…
stat.ML2026
Logarithmic-time Schedules for Scaling Language Models with Momentum
Damien Ferbach, Courtney Paquette, Gauthier Gidel +2
In practice, the hyperparameters and weight-decay in AdamW are typically kept at fixed values. Is there any reason to do otherwise? We show that for large-scale la…