> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenVLA
> A 7B-parameter open-source generalist manipulation policy trained on nearly 1 million real-world robot episodes from Open X-Embodiment.
[OpenVLA](https://openvla.github.io/) is the most influential open-source Vision-Language-Action model released to date. When it was first presented to the community, the project prompted a "record number of questions by far for any of the robot paper discussions" hosted by Hugging Face, a measure of how much excitement it generated among researchers.
Its popularity stems from filling a critical gap. Before OpenVLA, the most capable VLA models, such as Google's RT-2, were closed-source. Other open models were trained entirely on simulated data and did not generalize to real robots out of the box. OpenVLA gave the community the first powerful, fully open-source generalist manipulation policy.
## Main points
### Fully open-source and high capacity
OpenVLA is a **7-billion parameter model** built on a [Prismatic VLM](https://github.com/TRI-ML/prismatic-vlms) backbone, fusing **LLaMA 2** with **DINOv2** and **SigLIP** vision encoders. The researchers publicly released:
* All pre-training and fine-tuning code
* Model weights
* Data mixtures used for training
This level of openness, code, weights, and data, is what makes OpenVLA reproducible and extensible by the broader research community.
### Massive real-world pre-training
OpenVLA is trained on nearly **1 million real-world robot episodes** from the [Open X-Embodiment](https://robotics-transformer-x.github.io/) dataset, encompassing 27 different robotic datasets. This breadth allows it to control a variety of robots **out of the box**, including:
* WidowX
* Google RTX
* Franka Panda
The use of real (not simulated) demonstration data is a major reason it transfers well to physical hardware.
### State-of-the-art performance
When deployed out of the box, OpenVLA outperforms prior open-source models such as **Octo** and **RT-1X**, and even performs comparably to or better than the **55-billion parameter closed-source RT-2X** in most task categories. On average, it achieves a **20% higher absolute success rate**.
OpenVLA is particularly strong at:
* **Language grounding**, mapping instructions to the correct visual referents
* **Multi-instruction tasks with distractor objects**, staying on-task in cluttered scenes
### Trained via next-token prediction
OpenVLA treats robotic control as a **classification problem**, exactly like a text-based LLM:
1. The robot's 7-dimensional continuous action space (position, rotation, gripper state) is **discretized into 255 uniform bins**
2. The model predicts physical actions as standard text tokens using cross-entropy loss
3. **No architectural modification** of the underlying VLM is required
This is the same training paradigm as a language model, the only change is that some of the "tokens" in the vocabulary now represent robot actions.
### Highly accessible for low-compute budgets
You do not need a server cluster to use OpenVLA:
* **Parameter-Efficient Fine-Tuning (PEFT) with LoRA**, match full fine-tuning performance by training only **1.4% of the model's parameters**
* **4-bit quantization**, load and run the model on just **7 GB of GPU VRAM** (down from 16 GB) with no observed performance degradation
Together, these make OpenVLA accessible on consumer-grade GPUs, which is unusual for a 7B-parameter foundation model.
### Current limitations
Because OpenVLA is a large autoregressive model, it has several constraints in its current form:
* **Single-frame inputs only**, no temporal context across multiple frames
* **Single-step action prediction**, predicts one action at a time, no action chunking
* **Inference speed**, caps at roughly **3-9 Hz** depending on hardware
These limitations make OpenVLA currently unsuitable for high-frequency control tasks or complex bimanual manipulation without further optimizations such as action chunking, distillation, or more efficient inference backends.
## Further reading
* [OpenVLA project page](https://openvla.github.io/), paper, code, models, demos
* Kim et al. (2024). [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/abs/2406.09246)
* [Open X-Embodiment dataset](https://robotics-transformer-x.github.io/), the training corpus
* [Prismatic VLMs](https://github.com/TRI-ML/prismatic-vlms), the backbone architecture
***
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