FOAM folds Adam optimizer states into block-wise gradient means with residual correction, cutting memory overhead by up to 90% while matching vanilla Adam convergence rates under standard non-convex assumptions.
These details are provided in Table 10
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FOAM: Blocked State Folding for Memory-Efficient LLM Training
FOAM folds Adam optimizer states into block-wise gradient means with residual correction, cutting memory overhead by up to 90% while matching vanilla Adam convergence rates under standard non-convex assumptions.