[2606.05173] Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning
[Submitted on 16 Apr 2026]
Title:Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning
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Abstract:Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure. Inspired by the success of Joint Embedding Predictive Architectures (JEPA) (LeCun, 2022) in vision and audio, we propose a hybrid pre-training objective that combines a JEPA-style latent-space prediction loss with a standard MLM objective over a single shared encoder. A learnable scalar parameter continuously balances the two objectives during training. We pre-train both a hybrid model and a pure-MLM baseline on English Wikipedia using identical architectures and compute budgets (NVIDIA H100). Extensive representation analysis across five GLUE benchmarks (SST-2, MRPC, MNLI, CoLA, STS-B) using four pooling strategies reveals that the hybrid encoder produces significantly more uniform embeddings (uniformity less than -0.16 vs -0.05 for MLM), exhibits richer spectral geometry under max pooling, encodes less surface-level lexical information, and achieves a better semantic-to-lexical balance. Despite similar linear-probe downstream accuracy, the geometric differences are consistent and significant, suggesting that the JEPA predictive objective reshapes the latent space in ways that standard accuracy metrics alone cannot capture.
Comments: 12 pages, 10 figures, 11 tables. Preprint. Code available at : this https URL
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.05173 [cs.CL]
(or arXiv:2606.05173v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05173
arXiv-issued DOI via DataCite
Submission history
From: Aimen Boukhari [view email] [v1] Thu, 16 Apr 2026 22:26:47 UTC (1,440 KB)
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