Episode 3: Training a Robot’s Brain – OpenVLA’s Learning and Adaptation

Shaoqing Tan
Wed, 23 Jul 2025 06:03:00 GMT

Welcome back to our OpenVLA deep dive. In the last episode we figured out what the model’s parts are and how they operate. Now it’s time for the next logical question: How do you teach such a model to do all those tasks? Today we’re going to talk about OpenVLA’s training process—both its initial pretraining on a huge dataset and the ways we can fine-tune it afterward. If Episode 2 was the “hardware” (so to speak) of the brain, consider this the “education and practice” that made it smart.

Let’s start with the pretraining data because that’s truly the bedrock of OpenVLA’s capabilities. The creators of OpenVLA didn’t just gather a few demonstrations; they built on an unprecedented collaborative dataset called Open X-Embodiment (often shortened to OpenX). This is essentially a unification of robot demonstration datasets contributed by many different institutions (Google, Stanford, Berkeley, and more) into one giant pool. The result was about 970,000 robot manipulation trajectories covering a wide range of tasks, environments, and robot types. Think about that number: nearly one million episodes! These include everything from robot arms picking and placing objects on tables, to stacking blocks, opening drawers, wiping surfaces – you name it. And importantly, these data come from multiple embodiments: small robot arms like WidowX, larger industrial arms, maybe some mobile manipulator data, etc., each in various settings. Some data has natural language instructions annotated (like “place the apple in the bowl”), others might have goal indicators. OpenVLA’s training curated a mix of this data, ensuring that the model sees a diverse sample of the physical world of manipulation. Diversity is key – it’s what allows a single model to generalize across tasks and environments.

They trained OpenVLA on this dataset for about 15 days using 64 A100 GPUs (a serious amount of compute). During this training, the model weights were adjusted so that it gradually learned to map from (“instruction + image”) to (“action tokens”) that would successfully achieve the task in the training examples. One smart decision was that they didn’t train from scratch; they fine-tuned a pretrained model. Recall, OpenVLA’s architecture uses a Llama-2 language model and pretrained vision encoders. Initially, those components already knew a lot: Llama-2 knew language, and DINOv2/SigLIP knew vision. The training process thus was about connecting these to the robot actions. By the end of pretraining, OpenVLA essentially had absorbed the entire OpenX dataset’s worth of robot know-how into its parameters.

The result of this large-scale training was evident in performance: as we noted earlier, OpenVLA could zero-shot generalize to multiple robots and tasks. It’s like a student that attended a very good robotics school for a couple of years – it has a strong base knowledge. But what if you want to teach it something new after graduation, so to speak? That’s where fine-tuning comes in. A major focus of the OpenVLA project was to enable efficient adaptation of the model to new settings. Why is that important? Because no matter how big your pretraining data, a real robot deployment might have specifics that weren’t covered. Maybe you have a different robot arm that wasn’t in OpenX, or a unique task with custom objects, or you mounted the camera in a weird spot. In such cases, we don’t want to collect another million demonstrations to train a whole new model; we’d like to fine-tune the existing OpenVLA with perhaps just tens or hundreds of examples of the new scenario.

The team explored various fine-tuning strategies and made an encouraging discovery: using parameter-efficient fine-tuning methods, you can adapt OpenVLA very effectively by only tweaking a tiny fraction of its weights. One such method is LoRA (Low-Rank Adaptation). With LoRA, instead of modifying all 7 billion parameters, you insert some small trainable weight matrices (of much lower dimension) into each layer and only train those, leaving the original weights mostly frozen. OpenVLA’s researchers found that LoRA achieved the best trade-off – it matched the performance of full fine-tuning (where you’d adjust every parameter) while updating only 1.4% of the parameters! This is pretty astounding: it means you can fine-tune OpenVLA on a new task or robot without needing the computational resources to retrain a 7B model fully, and you only need to store a few million parameter differences for the new skill.

They demonstrated this by adapting OpenVLA to a completely new robot setup. In the paper, they took a Franka Emika Panda arm (a 7-DoF robot arm) in two scenarios: one, a standard tabletop setting, and two, a dynamic setup from a recent DROID dataset (which has the arm moving faster, at 15 Hz control). These Franka setups were not exactly covered in the original training (the OpenX dataset might have had some Franka data, but let’s assume these tasks/environments were new). Using a relatively small number of demonstrations for each new task, they fine-tuned OpenVLA. The result: the fine-tuned model on Franka achieved impressive success rates, often outperforming a strong baseline that they compared against. The baselines in those adaptation experiments included: (a) a state-of-the-art Diffusion Policy trained from scratch on that new task, and (b) an existing generalist model called Octo that was also fine-tuned on that data. OpenVLA generally came out on top. In fact, across multiple tested tasks, OpenVLA (with fine-tuning) was the only approach to get at least 50% success rate on every task, meaning it was consistently reliable whereas others failed completely on some harder tasks. And in the multi-object, instruction-driven tasks, OpenVLA’s advantage was significant: the diffusion policy baseline, which is trained from scratch and excels at very narrow, precise motions, couldn’t handle the diversity or language grounding as well – OpenVLA beat it by over 20% in success on average in those multi-task settings. This underscores that a well-trained foundation model can adapt better and faster than training a new model from zero, especially when the new tasks are complex or varied.

So practically, how would you fine-tune OpenVLA? The project released an open-source codebase and even example notebooks. You would gather, say, a small dataset of (image, instruction, actions) for your new task or robot. Then, using their code, you could apply LoRA fine-tuning on the OpenVLA checkpoint with that data. Because only 1.4% of parameters are being adjusted, this can be done on a single GPU in many cases (as long as the GPU has enough VRAM for a 7B model – 24GB is often enough with 4-bit quantization). They demonstrated that this process is accessible, not just a theory. This is a big deal: it means even smaller labs or companies without giant GPU clusters can take this large pre-trained model and specialize it to their needs.

Another aspect of training is serving or inference optimization. We touched on quantization: they showed you can quantize OpenVLA (e.g., 8-bit or 4-bit weights) and still get essentially the same success rates in tasks. This means you can deploy it on GPUs with limited memory, or possibly even on future specialized hardware, and still benefit from the full power of the model. The ability to run on “commodity” hardware is part of what makes OpenVLA practical.

Let’s not forget the dataset source: Open X-Embodiment. This initiative of sharing data is itself a fascinating story. Many groups contributed their previously siloed robot data to build that 970k trajectory corpus. The rationale was to enable exactly projects like OpenVLA and others. If you’re curious, OpenX includes data like Bridge datasets (multi-task teleop with a WidowX arm), Google’s robotics data (from the RT experiments), demonstrations from various labs, simulation rollouts, etc., all converted into a common format. It’s like the ImageNet moment for robotics – a large, community dataset to pretrain general models. OpenVLA is one of the first large models trained on OpenX, but certainly not the last. By the end of 2024, we also saw “Octo” (another model we’ll discuss soon) trained on OpenX data, and likely more to come. This trend suggests that rather than each lab collecting 1000 demos for their one-off model, pooling data to train foundation models will yield far more capable and reusable robotic brains.

One more interesting fine-tuning scenario: adapting to new modalities or outputs. The OpenVLA authors tried fine-tuning to new camera viewpoints and control modes (for example, one experiment added joint-position control data, another added force-torque sensor inputs). The model handled those extensions gracefully when fine-tuned. This modularity shows that once you have a core VLA model, you can plug in additional sensor data or different action types and it can incorporate them via additional training. It’s flexible.

To wrap up this training and adaptation discussion: The key points are that OpenVLA was pretrained on a massive, diverse dataset, which gave it strong general skills out-of-the-box, and it was explicitly designed to be adaptable via lightweight fine-tuning. It addresses the old robotics adage that “there’s no data like more data” by using tons of it upfront, and then addresses the counterpoint “my robot/environment is different” by allowing quick subsequent learning. The open-source nature means anyone can perform this fine-tuning for their custom use case. This drastically lowers the barrier to deploying advanced robot policies in new settings.

We’ve now covered what OpenVLA is and how it was trained. Up next, it’s time to see it in action—literally. In the following episode, we’ll discuss how OpenVLA is applied to real robot hardware, how it handles different robot embodiments, and some of the cool experiments and results demonstrated with it. In other words, theory meet practice! Stick around.

That’s it for the training class! We learned how OpenVLA was built on an enormous dataset and how we can fine-tune it without breaking the bank (or the GPU). The model’s ability to learn from nearly a million examples and then quickly adapt to new ones is a real game-changer for robotics development. In our next episode, we’ll switch gears from learning to doing: we’ll talk about deploying OpenVLA on actual robots, how it deals with different robot bodies, and what it looks like when this model runs in the real world. I’m excited for that—after all this talk of data and tokens, who isn’t ready to see some robots moving around? See you next time!