---
license: mit
language:
- en
pipeline_tag: robotics
library_name: transformers
tags:
- multimodal
- robotics
- vision-language-action
- openvla
- vla-arena
- imitation-learning
datasets:
- VLA-Arena/VLA_Arena_L0_L_lerobot_openpi
---

# OpenVLA (VLA-Arena Fine-tuned)

## About VLA-Arena

**VLA-Arena** is a comprehensive benchmark designed to quantitatively understand the limits and failure modes of Vision-Language-Action (VLA) models. While VLAs are advancing towards generalist robot policies, measuring their true capability frontiers remains challenging. VLA-Arena addresses this by proposing a novel structured task design framework that quantifies difficulty across three orthogonal axes:

1.  **Task Structure**: 170+ tasks grouped into four key dimensions:
    * **Safety**: Operating reliably under strict constraints.
    * **Distractor**: Handling environmental unpredictability.
    * **Extrapolation**: Generalizing to unseen scenarios.
    * **Long Horizon**: Executing complex, multi-step tasks.
2.  **Language Command**: Variations in instruction complexity.
3.  **Visual Observation**: Perturbations in visual input.

Tasks are designed with hierarchical difficulty levels (L0-L2). In this benchmark setting, fine-tuning is typically performed on **L0** tasks to assess the model's ability to generalize to higher difficulty levels and strictly follow safety constraints.

## Model Overview

The model is **OpenVLA** (7B), explicitly fine-tuned on demonstration data generated from **VLA-Arena**. It serves as a strong generalist baseline, leveraging the pre-trained knowledge of the original OpenVLA architecture while adapting to the specific safety and manipulation requirements of the benchmark.

This checkpoint was trained using **Low-Rank Adaptation (LoRA)** to efficiently adapt the large language backbone without full-parameter fine-tuning.

---

## Model Architecture

OpenVLA builds upon a strong Vision-Language Model backbone, directly tokenizing robotic actions into the language model's vocabulary.

| Component | Description |
| :--- | :--- |
| **Backbone** | **Llama 2 7B** (Language) + **SigLIP** (Vision) |
| **Action Space** | 7-DoF continuous control (Discretized into tokens) |
| **Adaptation Strategy** | **LoRA** (Rank $r=32$) |
| **Quantization** | None (Full precision loading during LoRA training) |

### Key Feature: Direct Action Prediction
OpenVLA treats robot actions as just another "language," allowing the model to utilize the massive reasoning capabilities of the Llama 2 backbone to plan and execute motion sequences based on visual inputs and textual instructions.

---

## Training Details

### Dataset
This model was trained on the **[VLA-Arena/VLA_Arena_L0_L_rlds](https://huggingface.co/datasets/VLA-Arena/VLA_Arena_L0_L_rlds)** dataset. The data consists of diverse robotic manipulation demonstrations covering the foundational **L0** tasks of the arena.

### Hyperparameters
The model was fine-tuned using LoRA on an 8-GPU cluster. Image augmentation was enabled to improve robustness against visual distractors.

| Parameter | Value |
| :--- | :--- |
| **Max Training Steps** | 200,000 |
| **Global Batch Size** | 128 (16 per device $\times$ 8 GPUs) |
| **Optimizer** | AdamW |
| **Learning Rate ($\eta$)** | $5.0 \times 10^{-4}$ |
| **Shuffle Buffer Size** | 100,000 |
| **Image Augmentation** | Enabled (`TRUE`) |

### LoRA Configuration
Unlike some lighter adaptations, this training run utilized a moderate rank without quantization to preserve model fidelity.

| Parameter | Value |
| :--- | :--- |
| **LoRA Rank ($r$)** | 32 |
| **LoRA Dropout** | 0 |
| **4-bit Quantization** | Disabled (`FALSE`) |

---

## Evaluation & Usage

This model is designed to be evaluated within the **VLA-Arena** benchmark ecosystem. It has been tested across 11 specialized suites with difficulty levels ranging from **L0 (Basic)** to **L2 (Advanced)**.

For detailed evaluation instructions, metrics, and scripts, please refer to the [VLA-Arena repository](https://github.com/PKU-Alignment/VLA-Arena).