Reactive Optimizers: rewrite the optimizer's source code during training
Researchers have introduced reactive optimizers that can alter their own source code while a model trains. The approach lets the optimizer adapt its update rules in response to observed gradients, potentially improving convergence speed and stability. Experiments on standard benchmarks show modest gains over static optimizers, and the method is implemented as a lightweight code‑generation layer integrated into existing training pipelines.