MGUP augments momentum optimizers with selective larger steps on a fixed proportion of parameters per iteration, claiming convergence guarantees for MGUP-AdamW and superior empirical performance on pretraining and fine-tuning.
Adasgd: Bridging the gap between sgd and adam
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MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
MGUP augments momentum optimizers with selective larger steps on a fixed proportion of parameters per iteration, claiming convergence guarantees for MGUP-AdamW and superior empirical performance on pretraining and fine-tuning.