ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling
Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies proposed pipeline parallelism to address the limited memory and computing resources of edge devices, they commonly rely on backpropagation (BP) training, which has a fundamental limitation of update locking and could experience severe throughput and memory bottlenecks. In this work, we propose a BP-free algorithm, called ZeroLock, that decouples the model updates into independent chunk updates by local objective construction. It breaks the update locking of BP and hence can improve throughput at the algorithm level and lower memory usage by redu...
| Authors | Wentao Dai, Xuanran Li, Yuxiang Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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