AdamW

AdamW is a variant of the Adam optimizer introduced by Loshchilov and Hutter in 2017 that decouples weight decay from the gradient-based parameter updates, producing better generalization and training stability than vanilla Adam with L2 regularization. The decoupling addresses a subtle bug in how Adam interacts with L2 regularization, where the adaptive learning rate scaling causes weight decay to be applied unevenly across parameters. AdamW applies weight decay as a separate explicit term, making it scale-invariant. AdamW has become the standard optimizer for LLM training including all major models from GPT-3 onward — GPT-4, Claude, Gemini, Llama, Mistral, Qwen, and most open-source models all use AdamW with weight decay typically in the 0.01-0.1 range. The optimizer is supported natively in PyTorch, JAX, TensorFlow, DeepSpeed, and every fine-tuning framework. AI governance teams document AdamW hyperparameters (learning rate, beta_1, beta_2, weight_decay, epsilon) as part of their training audit trail because these directly affect model behavior.

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