Yhn first seen 21 h ago, last 15 h ago, peak #25
Reactive Optimizers rewrite their own source code during training
Original: Reactive Optimizers: rewrite the optimizer's source code during training
Xingjian Bai has introduced Reactive Optimizers, a machine learning method in which the optimizer's own source code is rewritten while a model is being trained, allowing the training algorithm to adapt itself on the fly. The idea is being discussed by developers and machine learning researchers, with early reactions treating it as an intriguing but experimental take on optimizer design.
Why now: A novel, self-modifying approach to a core machine learning component is sparking curiosity and debate among researchers and engineers.
Xingjian Baimachine learningoptimizers
Evidence
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