Reactive Optimizers: rewrite the optimizer's source code during training
Summary
This post introduces reactive optimizers that place an agent inside the training loop to rewrite the optimizer's update rules in real time (OPAL). It demonstrates improved training outcomes across multiple tasks (image and language) and discusses practical considerations such as overhead, reproducibility, and real-world applicability, anchored by an open-source toolchain.