AdamW is a modification of the Adam optimizer that addresses the issue of weight decay being coupled with the adaptive learning rates. By decoupling these, AdamW applies weight decay directly to the weights, leading to more effective regularization and often better performance in practice.
AdamW is an optimization algorithm that decouples weight decay from the adaptive learning rate mechanism of Adam. This separation helps to prevent the optimizer from incorrectly decaying weights, leading to improved generalization and performance, especially in deep learning models.
| Alternative | Difference | Papers (with AdamW optimizer) | Avg viability |
|---|---|---|---|
| reinforcement learning | — | 1 | — |
| Group Relative Policy Optimization (GRPO) | — | 1 | — |