LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that allows large language models to be updated with fewer parameters, making post-training faster and more resource-efficient. Recent experiments show that LoRA can achieve performance comparable to full fine-tuning (FullFT) under certain conditions, particularly with small-to-medium-sized datasets, but may struggle with larger datasets and high batch sizes. Key findings suggest a "low-regret regime" where LoRA's efficiency aligns with FullFT, paving the way for its broader application in various scenarios.