Deep Double Descent

Deep Double Descent: Universal Phenomenon in CNNs, ResNets, and Transformers

OpenAI researchers demonstrate that the double descent phenomenon—where model performance improves, degrades, then improves again—occurs universally across CNNs, ResNets, and transformers as a function of model size, data size, or training time. The effect can often be masked by careful regularization, which may explain why it has been underappreciated. The underlying mechanism remains poorly understood, and the authors identify it as an important open research direction.