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README.md

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+# 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}, 
+}
+```