Reactive optimizer uses language model to improve training automatically
Reactive Optimizers
Machine Learning
Summary
Training AI models involves choosing how the model’s parameters change during learning, using something called an optimizer. The authors study a new kind of optimizer that watches the training process and updates its own code on the fly, using a language-model agent to decide how to adjust learning rates and other rules. This approach works without needing extra training for these adjustments and can handle unexpected problems like data errors. The authors show it performs as well or better than fixed optimizers on language and image tasks.
What this means in practice
- •For machine learning engineers: Automatically adjust optimizer settings during training to handle new tasks without manual tuning.
- •For computer graphics teams: Improve image generation quality by adaptively changing optimizer rules in response to training challenges.
Authors
Xingjian Bai, Jia-Hao Liang, Jayden Teoh, Vincent Sitzmann
Abstract
We study a reactive optimizer that uses a pretrained language-model agent to inspect training dynamics and rewrite executable optimizer code during a single training run. The agent can change update rules and schedules for individual parameter groups, without optimizer-specific meta-training or memory transferred from other runs. We evaluate this approach, OPAL, on language modeling and image generation when a recipe has not been tuned for the target setting. Across the evaluated settings, it matches or improves on selected fixed recipes. Analysis of its decisions shows changes to learning-rate schedules, optimizer structure, numerical precision, and momentum. Controlled experiments also show adaptation to repeated data corruption and reduced overfitting. We report these behaviors together with the agent's computational cost and implementation requirements.