⛳ Optimizer: What Does It Do and Why We Need It

Optimizers play a crucial role in training large language models like GPT by managing the complex loss landscapes that such models encounter. Stochastic Gradient Descent (SGD), a basic optimization technique, often struggles with issues such as getting stuck in shallow valleys, thrashing in narrow ravines, and making slow progress on plateaus. To address these challenges, advanced optimizers like Momentum and RMSProp were developed, introducing concepts such as accumulated gradients and adaptive learning rates to improve training efficiency. The Adam optimizer combines these ideas, using both momentum and adaptive learning rates to navigate varying terrains effectively, making it the default choice for neural network training despite its high memory cost. Nonetheless, the search for more efficient optimizers continues, with alternatives like the Muon optimizer being explored to reduce memory demands while retaining performance.