Proving and Improving the Platonic Representation Hypothesis with JEPAs
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper proves a Platonic Representation Theorem: JEPAs trained separately on any modalities learn the same representation up to one rotation when positive pairs are good views of one scene, with bounded misalignment otherwise. It argues next-token prediction is a JEPA with bad views, explaining why language models align with vision only through scale. Validation spans 145k captions to 940M web images, reporting a scaling law of alignment. A 109M-parameter text JEPA reportedly aligns better with DINOv2 and CLIP image encoders than language models up to 72B parameters.
One primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourcehttps://github.com/galilai-group/prh-proof
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The paper proves a Platonic Representation Theorem: JEPAs trained separately on any modalities learn the same representation up to one rotation when positive pairs are good views of one scene, with bounded misalignment otherwise. It argues next-token prediction is a JEPA with bad views, explaining why language models align with vision only through scale. Validation spans 145k captions to 940M web images, reporting a scaling law of alignment. A 109M-parameter text JEPA reportedly aligns better with DINOv2 and CLIP image encoders than language models up to 72B parameters.
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Key findings from the stored brief
- Separately trained JEPAs on good-view positive pairs learn the same representation up to one rotation, per the stated theorem.
- Good views reportedly need 42 times fewer training samples than next-token prediction to reach the same alignment.
- A 109M-parameter text JEPA reportedly aligns with DINOv2 and CLIP image encoders better than language models up to 72B parameters.
- One fitted rotation turns separately trained encoders into an image captioner, reporting CIDEr .838 on COCO.
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Why this may matter
If the theorem and efficiency claims hold, they suggest positive-view design, not scale alone, may be a practical lever for building aligned cross-modal representations more efficiently than next-token training.
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