arXiv:2506.17639v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation. However, their large parameter sizes and high inference latency hinder real-world deployment, especially on resource-constrained platforms. To address this, we conduct a systematic empirical study of model compression for VLAs. Building on these insights, we present \textit{RLRC}, a three-stage compression and recovery pipeline consisting of structured pruning, performance recovery via SFT and RL, and subse
The proliferation of advanced AI models like VLAs is creating an urgent need for efficient deployment solutions on diverse hardware, driving research into model optimization and compression.
This research addresses a critical bottleneck for real-world AI deployment, enabling powerful models to run on resource-constrained platforms, which is essential for scaling applications in areas like robotics.
The ability to significantly compress Vision-Language-Action models while maintaining performance makes advanced robotic manipulation more accessible and feasible for broader adoption outside of high-compute environments.
- · Robotics companies
- · Edge AI developers
- · Hardware manufacturers (specialized for inference)
- · AI model developers
- · Companies reliant on large, unoptimized models
- · Competitors without efficient compression techniques
More sophisticated robotic systems can be deployed in diverse, resource-limited environments.
The cost of deploying advanced AI in robotics decreases, accelerating the adoption of autonomous agents in various industries.
Increased accessibility and efficiency of robotic AI could lead to new forms of human-robot collaboration and increased automation in complex tasks.
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Read at arXiv cs.AI