This article details a reproducible, 100-step LoRA fine-tuning run for the OpenVLA robot AI model using Google Colab. The author aimed to demonstrate a feasible path for understanding OpenVLA fine-tuning before committing to larger experiments. The process involved setting up Colab, verifying dataset loading, confirming GPU training, and providing inspectable evidence of the run.

OpenVLA, a 7-billion-parameter vision-language-action model, is designed for robot control. Fine-tuning involves adapting a pre-trained model to a specific dataset of robot demonstrations. This guide focuses on using Low-Rank Adaptation (LoRA), which trains only a small set of adapter weights, making the process more accessible and memory-efficient. The experiment utilized the `openvla/openvla-7b` checkpoint and the `libero_spatial_no_noops` Robot Learning Dataset Standard (RLDS) dataset.

The article walks through the entire workflow, from setup to evidence logging in Weights & Biases (W&B). It explains the dataset format, the training command, key hyperparameters, and how to interpret training metrics like loss and action accuracy. The goal is to provide a verifiable foundation, not a performance benchmark, enabling users to confirm that the fine-tuning process works as expected before undertaking more extensive evaluations.