Foundation Models

Fine-Tuning

Fine-tuning is the process of continuing to train a pretrained model on a smaller, task- or domain-specific dataset so it specializes while retaining broadly useful representations. In robotics, vision-language-action models pretrained on large cross-embodiment corpora such as Open X-Embodiment are fine-tuned on demonstrations from a target robot and task, with variants including full-parameter updates, parameter-efficient methods like LoRA, and post-training with reinforcement learning.

Why it matters for physical AI

Pretraining supplies general visuomotor priors, but deployments succeed or fail on adaptation; fine-tuning recipes determine how many demonstrations, how much compute, and how much forgetting a new skill costs.