Skip to content
Sagan

Paper

Representation Without Reward: A JEPA Audit for LLM Fine-Tuning

Unreadunread

AI summary

arXiv:2605. 15394v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) propose that a model should learn more useful abstractions when trained to predict latent representations rather than observed outputs.