Parameter-efficient fine-tuning (PEFT) adapts a large pretrained model to a new task by training only a small number of parameters while the original weights stay frozen. Low-Rank Adaptation (LoRA) is the most widely used method. Hu et al. introduced it in "LoRA: Low-Rank Adaptation of Large Language Models" (2021): it "freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture". For a weight matrix W, LoRA learns an update BA whose inner rank r is small. After training, BA can be merged into W, so a merged adapter adds no inference latency.
For VLAs, LoRA cuts the memory needed to fine-tune, because gradients and optimizer state exist only for the adapter. The OpenVLA paper (Kim et al., 2024) reports fine-tuning its 7B model "on consumer GPUs via modern low-rank adaptation methods". LeRobot v0.6.1 integrates the Hugging Face PEFT library as lerobot[peft]; its example fine-tunes SmolVLA on LIBERO with --peft.method_type=LORA --peft.r=64 --peft.lora_alpha=64, where the scaling factor is lora_alpha / r.
Two misconceptions recur. First, LoRA reduces training memory, not deployment memory: inference still loads the full base weights, so a LoRA-tuned π0 needs the same GPU memory at run time as the base model. Lower deployment memory comes from quantization, not LoRA. Second, LoRA is not free in quality; the LeRobot docs note that the higher the rank, the closer the result gets to full fine-tuning. Whether a result used LoRA or full fine-tuning is part of what makes it reproducible on a given GPU, such as an RTX 4090.