Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning 事件

Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning arXiv:2605.24058v1 Announce Type: cross Abstract: On-device adaptation of large language models commonly keeps a quantized base model frozen while training and deploying a small, task-specific LoRA adapter. In the unmerged adapter-mode setting, however, the adapter is more than a compact storage module; it introduces an additional dense floating-point branch, maintains a trainable state for local updates, and acts as