Don’t Just “Throw Adam at It”: Misunderstanding Adam Will Cost You

Blindly using the Adam optimizer with default parameters is a common but dangerous practice that can lead to model training failure, especially in complex reinforcement learning or large model scenarios. The algorithm works by using first moment (momentum) and second moment (variance) estimates to adapt the learning rate for each parameter, smoothing noisy gradients and adjusting step sizes. However, this adaptive mechanism can fail spectacularly by not converging on simple problems, finding suboptimal solutions, or causing training instability and loss spikes in large models. Understanding and tuning Adam's hyperparameters, such as β₁ and β₂, is critical for navigating difficult optimization landscapes and achieving successful results.