EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
📰 ArXiv cs.AI
Learn how EmbodiedMidtrain bridges the gap between Vision-Language Models and Vision-Language-Action Models via mid-training, improving downstream performance
Action Steps
- Characterize the data distribution gap between Vision-Language Models and Vision-Language-Action Models
- Apply EmbodiedMidtrain to adapt Vision-Language Models to the embodied domain
- Fine-tune the adapted model on Vision-Language-Action tasks
- Evaluate the performance of the adapted model on downstream tasks
- Compare the results with off-the-shelf Vision-Language Models
Who Needs to Know This
AI researchers and engineers working on Vision-Language-Action Models can benefit from this approach to improve their model's performance, especially those working on embodied AI applications
Key Insight
💡 EmbodiedMidtrain adapts Vision-Language Models to the embodied domain, improving their performance on Vision-Language-Action tasks
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🤖 EmbodiedMidtrain bridges the gap between Vision-Language Models and Vision-Language-Action Models via mid-training! 🚀 Improve your model's performance with this novel approach #AI #EmbodiedAI
Key Takeaways
Learn how EmbodiedMidtrain bridges the gap between Vision-Language Models and Vision-Language-Action Models via mid-training, improving downstream performance
Full Article
Title: EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Abstract:
arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are not adapted to the embodied domain, limiting their downstream performance. In this work, we propose EmbodiedMidtrain to bridge the gap between VLMs and VLAs. We first characterize the data distribution gap between them, showing that VLA data occupy compact regions that a
Abstract:
arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are not adapted to the embodied domain, limiting their downstream performance. In this work, we propose EmbodiedMidtrain to bridge the gap between VLMs and VLAs. We first characterize the data distribution gap between them, showing that VLA data occupy compact regions that a
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