RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

Understand

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 subsequent quantization.