Separating Representation from Reconstruction Enables Scalable Text Encoders
While decoders have rapidly scaled, encoders have remained largely unchanged since BERT. We revisit this disparity by frozen backbone evaluation via probing. Under this lens, the representations of BERT encoders become increasingly $\textit{unexploitable}$ by frozen probes, despite improved perplexity. The misalignment originates in BERT's flat design, which couples representation learning to the token reconstruction loss. We propose $\textbf{CrossBERT}$, a two-part architecture that separates the learning of high-quality encoded representations from the rigid grounding of token reconstruction. This design further enables high masking ratios ($\ge 50\%$) and gradient collection over all t...
| Authors | Megi Dervishi, Mathurin Videau, Yann LeCun |
|---|---|
| Primary category | cs.CL |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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