Let me know if you have any questions!

I knew close to nothing about JEPA before reading your post, but i was under the impression this was supposed to be a contender for LLM's limitations, a way to do things "properly".

However, based on this article, i have the feeling that on the contrary, JEPA is filling a super niche problem, and is pretty much only trying to improve robotic motions in space.

Which of the two ideas is it ? Can JEPA technics be applied to a much more wide range of problems ? (like LLM and transformers)

	▲	LarsDu88 6 hours ago \| parent [-]
		JEPA can be applied to text as well in the sense you can use it to build text embedding models, freeze your JEPA text embedder, then train a decoder over the top of that. OF course your decoder model could end up doing a lot of the heavy lifting if your decoder loss was based on text reconstruction methods (which almost all LLMs use for eval purposes) For certain tasks, you do need pixel/token level reconstruction fidelity to do a lot of things. Like image generation or text generation. For other tasks, having a coherent representation of the world that can be computed quickly and intuitively is more salient. I suspect the human mind works more like JEPA than like an autoregressive LLM. Human children train on far less data to achieve extreme fidelity on a lot of tasks generative reconstruction loss based AI/ML techniques struggle with.