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arXiv · 2601.00366

BERT-JEPA: Reorganizing CLS Embeddings for Language-Invariant Semantics

Taj Gillin·Adam Lalani·Kenneth Zhang·Marcel Mateos Salles
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Abstract

Joint Embedding Predictive Architectures (JEPA) are a novel self supervised training technique that have shown recent promise across domains. We introduce BERT-JEPA (BEPA), a training paradigm that adds a JEPA training objective to BERT-style models, working to combat a collapsed [CLS] embedding space and turning it into a language-agnostic space. This new structure leads to increased performance across multilingual benchmarks.

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Taj Gillin, Adam Lalani, Kenneth Zhang, Marcel Mateos Salles. 2026-01-01. BERT-JEPA: Reorganizing CLS Embeddings for Language-Invariant Semantics. https://arxiv.org/abs/2601.00366

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