Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning 事件

Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning arXiv:2602.13069v2 Announce Type: replace-cross Abstract: On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6--12GB shared across all workloads. Existing approaches force a trade-off between exact gradients with high memory (MeBP) and low memory with noisy estimates (MeZO). We propose Memory-efficient Structured Backpro