How the Adam Optimizer Became the Backbone of Modern AI Training
Adam (Adaptive Moment Estimation), an optimization algorithm proposed by Diederik P. Kingma and Jimmy Ba in 2014, has become the default training method for most large language models, including ChatGPT, Claude, and Llama. Training deep neural networks requires updating billions of parameters across trillions of tokens, making the choice of optimizer a critical engineering challenge. Earlier methods like basic gradient descent and SGD struggled with noisy updates and vastly different gradient magnitudes across parameters. Adam solved these problems by combining momentum, which smooths noisy gradient updates, with adaptive per-parameter learning rates borrowed from algorithms like AdaGrad and RMSProp. By maintaining two running statistics per parameter, Adam adjusts update sizes individually, making large-scale model training far more stable and efficient.
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