Directly updating bf16 weights with AdamW causes weight updates to fail if the change is less than approximately 1/512 of the weight's magnitude. This creates an unintended sparse fine-tuning effect where only small-magnitude weights evolve while larger weights remain frozen. Using higher-precision optimizers requires a 3x to 10x reduction in learning rate to match performance.