LoRA

Low-Rank Adaptation — a parameter-efficient fine-tuning method that freezes the pre-trained model weights and injects trainable low-rank matrices into each layer. LoRA dramatically reduces the number of trainable parameters (often 1-10% of full fine-tuning) while achieving comparable performance. It is increasingly used for adapting large VLA models to specific robot embodiments or tasks.