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

@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}, 
}