Shengyun Si

“VLA-Pro stores task-specific LoRA adapters as parameterized procedural memories during training. At inference time, VLA-Pro retrieves relevant procedural memories based on the current multi-modal context and dynamically fuses these memories for generating the current action chunk.”

“Without VLA-Pro, a state-of-the-art VLA model achieves only a 5.8% success rate on unseen real-world manipulation tasks. With VLA-Pro, that climbs to 65.0% — an 11x improvement.”