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cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models
TL;DR
ER-JEPA enhances the LLM-JEPA model by incorporating experience replay, which improves the accuracy and stability of predictions in language models. This approach consistently outperforms its predecessor across various datasets.
✦ Why It Matters
Consider implementing experience replay in your language model training to enhance prediction accuracy.
Key Takeaways
- Explore using experience replay in your models for better performance.
- Test ER-JEPA on your datasets to evaluate improvements.
- Align different knowledge views to enhance model understanding.
- Utilize episodic memory for additional training supervision.
Full Summary
Large language models (LLMs) are proficient in generating text at the token level but often struggle with abstract semantics and comprehensive understanding. LLM-JEPA addresses these issues by using a joint-embedding predictive architecture (JEPA) to align different perspectives of the same knowledge.
However, achieving strong alignment does not guarantee accurate predictions. To improve this, ER-JEPA introduces an episodic replay mechanism that stores training pairs in memory, allowing the model to retrieve relevant data for additional supervision during training.
This dual learning approach, utilizing both current batches and stored pairs, enhances token prediction and representation alignment. Experiments on datasets like NL-RX, GSM8K, Spider, and NQ-Open show that ER-JEPA consistently outperforms LLM-JEPA, indicating its effectiveness in improving language model performance.