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license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-0.5B
---
# Model Card for MergeVLA-LIBERO
MergeVLA — Single-Skill Experts for Spatial / Object / Goal / Long-10 (LIBERO Task Suite). These models are used as the base expert checkpoints for our MergeVLA.
## Model Details
Each uploaded model is a 0.68B-parameter VLA model *(excluding the vision backbone)* composed of:
- Qwen2.5-0.5B as the Vision-Language Model (VLM)
- A lightweight 0.18B Action Expert
- A two-layer Proprioceptive Projector MLP
### ✔️ **Performance (Success Rates on LIBERO)**
| Task Family | Success Rate (%) |
| ----------- | ---------------- |
| **Spatial** | **98.0** |
| **Object** | **98.6** |
| **Goal** | **95.0** |
| **Long-10** | **95.0** |
### 🧠 **Training Details**
Each expert is fine-tuned independently using modified LIBER demonstrations in RLDS format.
| Category | Value |
| ----------------------- | ------------------------ |
| LoRA | Enabled (rank = 64) |
| Optimizer | AdamW |
| Learning Rate | 2e-4 |
| Batch Size | 8 (×2 grad accumulation) |
| num_images_in_input | 2 |
### **Training Steps**
* **Spatial** — 30,000
* **Object** — 20,000
* **Goal** — 30,000
* **Long-10** — 50,000
## Citation instructions
```BibTeX
@misc{fu2025mergevla,
title={MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent},
author={Yuxia Fu and Zhizhen Zhang and Yuqi Zhang and Zijian Wang and Zi Huang and Yadan Luo},
year={2025},
eprint={2511.18810},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2511.18810},
}
``` |