Agent rewrites optimizer code during training: 'reactive optimizer' matches tuned recipes
Kyrannio · x · 2026-10-22
Researchers propose a 'reactive optimizer': instead of a static optimizer, an agent watches training dynamics and rewrites the optimizer's source code mid-training. On every language and image task without a tuned recipe, it matches or beats the best one-shot recipe, and it self-guards against loss spikes and overfitting. The authors argue optimization decisions may soon belong to agents. Paper and code are available to try on your own tasks.
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