ZeroLock decouples LLM fine-tuning into independently updated chunks using local objectives and a pipeline system, achieving moderate memory and throughput gains, but its convergence proof targets a surrogate objective rather than the true global loss.
GPipe: Efficient training of giant neural networks using pipeline parallelism,
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ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling
ZeroLock decouples LLM fine-tuning into independently updated chunks using local objectives and a pipeline system, achieving moderate memory and throughput gains, but its convergence proof targets a surrogate objective rather than the true global loss.