From Pixels to Actions - Towards Native Vision-Language-Action Policies
NEO-VLA is a vision-language-action framework that removes the fixed visual encoder.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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NEO-VLA is a vision-language-action framework that removes the fixed visual encoder. Images are tokenized with lightweight patch convolutions and evolve jointly with language through the backbone, letting a flow-matching action expert read instruction-conditioned visual features at different depths. Under controlled comparisons, native architectures outperform encoder-based counterparts; full fine-tuning narrows but does not erase the gap, and scale alone does not recover it. Layer-wise probing shows action-critical signals often peak before the final visual representation. The model is reported to be robust to visual shifts but challenged by changes in spatial configurations and task goals.
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Native patch-convolution tokenization outperforms encoder-based VLMs under controlled comparisons.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseFull fine-tuning reduces but does not erase the native-architecture gap; added scale alone does not close it.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseAction-critical visual signals often peak well before the final visual representation in layer-wise probing.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseNEO-VLA is robust to visual shifts but remains challenged by changes in spatial configurations and task goals.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
The work questions the standard VLA premise that perception must be compressed by a fixed visual encoder before action reasoning. If action policies benefit from access to intermediate visual computation, architectures for embodied control may need to change how visual representations are formed.
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