LeCun力荐的JEPA杀入LLM，用CV的思路训练LLM，性能鲁棒性双丰收

Core Viewpoint - The article discusses the introduction of LLM-JEPA, a new architecture that extends the Joint Embedding Predictive Architecture (JEPA) concept from the visual domain to large language models (LLMs), enhancing their performance and robustness in various tasks [8][10][12]. Group 1: Introduction of LLM-JEPA - LLM-JEPA is based on the JEPA concept, which aims to efficiently learn world knowledge by predicting future or missing features in an abstract representation space [7][8]. - The architecture successfully applies the JEPA target to LLMs by treating data pairs (text, code) as different views of the same underlying knowledge [8][10]. Group 2: Performance and Validation - Experimental results show that LLM-JEPA significantly outperforms standard LLM training objectives, demonstrating strong robustness against overfitting [10][11]. - The method has been validated across various mainstream model series and diverse datasets, including Llama3, OpenELM, and Rotten Tomatoes [11][21]. Group 3: LLM-JEPA Objective Function Design - The LLM-JEPA objective function retains the generative capabilities of LLMs while enhancing their abstraction capabilities through joint embedding predictive tasks [15][16]. - The design incorporates a loss function that balances traditional LLM loss with the JEPA target, allowing for a unified approach to different types of views [15][16]. Group 4: Empirical Results - LLM-JEPA has shown to improve fine-tuning outcomes across multiple pre-trained LLMs and datasets, with performance enhancements observed in various configurations [21][23]. - The architecture also demonstrates improved pre-training effectiveness, leading to higher quality representations compared to traditional methods [32][34]. Group 5: Future Directions and Limitations - The research team plans to conduct larger-scale tests to further explore the potential of LLM-JEPA, despite current limitations such as increased computational costs due to the need for multi-view representations [35][36]. - Concerns have been raised regarding the method's reliance on paired data, which may limit its generalizability and practical application [36].