@alesfav: AI needs vastly more data than...
@alesfav
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Jun 22, 2026
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AI needs vastly more data than we do. One idea might close the gap: don't predict raw signals (tokens), predict your own abstract latent representation (JEPA, data2vec).
With @DanKorchinski @MatthieuWyart, on a toy model, we prove how much that helps: the gap is exponential.
🧵
With @DanKorchinski @MatthieuWyart, on a toy model, we prove how much that helps: the gap is exponential.
🧵
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We study recovering the hidden latent tree of a hierarchical grammar.
Token-level SSL pays a depth tax: the data it needs grows exponentially with the tree's depth. We prove that iteratively supervising on latents escapes it, recovering the tree with constant-in-depth data!
Token-level SSL pays a depth tax: the data it needs grows exponentially with the tree's depth. We prove that iteratively supervising on latents escapes it, recovering the tree with constant-in-depth data!
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Surprisingly, we found data2vec already does this with a single module. Through its teacher, it implicitly supervises on latents at every level, reaching the same constant-in-depth scaling. 🤯
The hierarchy unfolds during training rather than being stacked into the architecture.
The hierarchy unfolds during training rather than being stacked into the architecture.
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This result also suggests that explicit stacking, like H-JEPA, may be redundant.
Many open questions!
📄 Our paper: arxiv.org/abs/2605.27734
Many open questions!
📄 Our paper: arxiv.org/abs/2605.27734
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@TMoldwin @DanKorchinski @MatthieuWyart Latent prediction avoids that bottleneck by learning one level, then using that learned level as the target/context for the next.
We may write a more accessible blog post version at some point!
We may write a more accessible blog post version at some point!