MoFaSGD keeps a low-rank factored momentum and uses its singular vectors as the update direction, achieving LoRA-level memory with competitive fine-tuning performance, but its convergence proof is flawed.
Memory efficient adaptive optimization
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Low-rank Momentum Factorization for Memory Efficient Training
MoFaSGD keeps a low-rank factored momentum and uses its singular vectors as the update direction, achieving LoRA-level memory with competitive fine-tuning performance, but its convergence proof is flawed.