Anchor the Semantics, Align the Actions: A Better Way to Fine-Tune VLAs
Proposes a VLA fine-tuning technique that preserves pretrained VLM representations while aligning language to actions, raising real-robot generalization success from 28% to 54%.
The skinny
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