ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models
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Computer Science > Computation and Language
arXiv:2609.36952 (cs)
[Submitted on 29 Sep 2026]
Title:ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models
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Abstract:Large language models (LLMs) excel at token-level generation but may learn undesirable abstract semantics and lack comprehensive perception. LLM-JEPA mitigates this by aligning different views of the same underlying knowledge via a joint-embedding predictive architecture (JEPA). However, strong alignment does not necessarily lead to accurate, stable predictions. To address this, we propose ER-JEPA, which adds an episodic replay path to LLM-JEPA. ER-JEPA stores training pairs in a memory. At each step, it stores and retrieves relevant data to provide additional supervision. This enables learning from both the current batch and stored training pairs, providing additional supervision for token prediction and representation alignment. Experiments across multiple datasets (NL-RX, GSM8K, Spider, and NQ-Open) demonstrate that ER-JEPA consistently outperforms LLM-JEPA.
| Comments: | 20 pages, 15 figures, 6 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.36952 [cs.CL] |
| (or arXiv:2609.36952v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.36952
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models, by Jingnan Pu and 2 other authors
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