Patch Policy beats a fine-tuned 7B VLA by 18% with 0.7% of the parameters
ylecun · x · 2026-07-23
Researchers introduce Patch Policy, a minimal architectural extension for robot policies that lets transformer-based agents consume dense ViT patch tokens directly instead of collapsing vision into a single vector first.
- The paper argues that pretrained ViTs preserve rich spatial detail that standard policies throw away.
- Patch Policy reaches 18% better performance than a fine-tuned 7B VLA while using only about 0.7% of its parameters.
- The result is presented as a path to more robust and precise manipulation without needing a billion-parameter vision-language model.
Related event: Patch Policy Outperforms 7B VLA with 0.7% Parameters(5 posts)→
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