ProteinJEPA: Latent prediction improves protein language model pretraining
Protein language models are trained primarily with masked language modeling (MLM), which predicts masked amino-acid identities. Joint-embedding predictive architectures (JEPA) instead predict latent representations, but have not been applied to proteins. ProteinJEPA supplements MLM with a cosine loss for predicting the half-depth hidden states of a teacher given the unmasked sequence. On 19 tasks, with ESM2 at 35M and 150M parameters and three pretraining seeds, MLM+JEPA outperforms compute-matched and step-matched MLM-only continued training in 78 and 76 of 114 comparisons (14 losses, 22 ties). The median compute-matched gain is $+0.0106$ on structure- and homology-sensitive tasks versus...
| Authors | Dan Ofer, Dafna Shahaf, Michal Linial |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 23 September 2026 |
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