PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
This addresses the low-rank bottleneck in parameter-efficient fine-tuning for LLMs, offering an incremental improvement over existing LoRA methods.
The paper tackles the performance gap between LoRA and full fine-tuning in large language models by proposing PeriodicLoRA (PLoRA), which accumulates low-rank updates over multiple stages to achieve higher rank, resulting in up to 1.8 times stronger learning ability without increased memory usage.
Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Recently, parameter-efficient fine-tuning (PEFT) methods have been widely studied due to its cost-effectiveness. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low-dimensional. Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices. In order to break the low-rank bottleneck in LoRA Optimization, we propose PeriodicLoRA (PLoRA), which accumulates low-rank update matrices multiple times to achieve a higher update rank. PLoRA has multiple training stages. During each stage, we still update only the LoRA weights. However, at the end of each stage, we unload the LoRA weights into the backbone parameters and then reinitialize the LoRA states. Experimental results show that PLoRA has stronger learning ability, approximately 1.8 times that of LoRA's learning ability at most, but it does not increase memory usage. Further, we introduce a momentum-based unloading strategy for PLoRA to mitigate the training instability.