Training Tips and Tricks: LR Scheduling, Gradient Clipping and Mixed Precision
~13 min read
Three practical techniques that make real training runs work: learning rate scheduling (change the step size over time), gradient clipping (cap runaway gradients), and mixed precision (train faster with lower-precision numbers).
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Key points
- •Learning rate scheduling changes step size over training: warmup (small -> larger, early stability) then decay (larger -> smaller, precise late refinement)
- •Cosine or linear decay lets early steps make big exploratory progress and later steps fine-tune near the minimum
- •Gradient clipping caps a gradient vector's magnitude (rescaling, keeping direction) to prevent occasional huge 'exploding gradients' from wrecking the weights
- •Mixed precision training runs most computation in 16-bit floats instead of 32-bit, roughly halving memory and speeding up GPU computation
- •Mixed precision keeps sensitive operations in 32-bit for stability — the same 'fewer bits, faster/cheaper' idea as quantization, but applied during training rather than inference