auxiliary-objectives

This paper audits Joint-embedding predictive architectures (JEPA) for LLM fine-tuning on a natural-language-to-regex task, testing twenty-two auxiliary objectives. The results show that hidden-state representation improvements are only weakly coupled to decoded-task accuracy, with no auxiliary surviving family-wise correction.