Optimizing ML Training with Metagradient Descent
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909540904075264 |
|---|---|
| author | Engstrom, Logan Ilyas, Andrew Chen, Benjamin Feldmann, Axel Moses, William Madry, Aleksander |
| author_facet | Engstrom, Logan Ilyas, Andrew Chen, Benjamin Feldmann, Axel Moses, William Madry, Aleksander |
| contents | A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for efficiently calculating metagradients -- gradients through model training -- at scale. We then introduce a "smooth model training" framework that enables effective optimization using metagradients. With metagradient descent (MGD), we greatly improve on existing dataset selection methods, outperform accuracy-degrading data poisoning attacks by an order of magnitude, and automatically find competitive learning rate schedules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing ML Training with Metagradient Descent Engstrom, Logan Ilyas, Andrew Chen, Benjamin Feldmann, Axel Moses, William Madry, Aleksander Machine Learning Artificial Intelligence A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for efficiently calculating metagradients -- gradients through model training -- at scale. We then introduce a "smooth model training" framework that enables effective optimization using metagradients. With metagradient descent (MGD), we greatly improve on existing dataset selection methods, outperform accuracy-degrading data poisoning attacks by an order of magnitude, and automatically find competitive learning rate schedules. |
| title | Optimizing ML Training with Metagradient Descent |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.13751 |