Learning rate schedules adjust learning rate during training to improve convergence and final performance. Why schedule: High LR early for fast progress, lower LR later for fine-grained optimization. Fixed LR may oscillate or plateau. Common schedules: Step decay: Reduce LR by factor at specific epochs. Simple but discontinuous. Cosine annealing: Smooth cosine decay to near-zero. Popular for vision and LLMs. Linear decay: Constant decrease. Often used after warmup. Exponential decay: Multiply by constant each step. Inverse sqrt: LR proportional to 1/sqrt(step). Common for transformers. Warmup + decay: Warmup to peak, then decay. Standard for LLM training. Choosing schedule: Cosine is safe default. Experiment if training plateaus or diverges. One-cycle: Peak in middle, aggressive decay at end. Can improve convergence. Implementation: PyTorch schedulers (CosineAnnealingLR, OneCycleLR), TensorFlow schedules. Interaction with optimizer: Adaptive optimizers (Adam) already adjust effectively, but schedule still helps. Tuning: LR is most important hyperparameter. Schedule is second-order but impactful.