Fine-Tuning

Adapting a pre-trained model to a specific downstream task by continuing training on task-specific data, typically with a lower learning rate. In robot learning, fine-tuning a pre-trained visual encoder or VLA on robot demonstration data is the standard transfer learning approach. Full fine-tuning updates all parameters; parameter-efficient methods (LoRA, adapters) update only a subset.