Fangzheng Feng

FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence

Parameter-efficient fine-tuning (PEFT) methods have emerged as a practical solution for adapting large foundation models to downstream tasks, reducing computational and memory costs by updating only a small subset of parameters. Among them, approaches like LoRA aim to strike a balance between effici