OPAL: an agent rewrites optimizer source code during training, beating static recipes
vincesitzmann · x · 2026-10-22
A viral thread argues most autoresearch today is glorified overfitting—recipes tuned for one setting rarely transfer. The team behind OPAL proposes inner-loop autoresearch: an agent watches training dynamics and rewrites the optimizer's source code mid-training, forming a reactive optimizer. They report it matches or beats the best one-shot recipe across language and image tasks with no tuned recipe, while self-guarding against loss spikes and overfitting. Code and paper are open.
Related event: EPFL's OPAL Puts a Coding Agent Inside the Training Loop(8 posts)→
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