[vatsachak]

Are you joking? They spend billions of dollars training LLMs to get a 7.8% on arc agi 3 whereas DINO models are near sota in image classification, provide meaningful embeddings to the point where image segmentation is just PCA. The spend on DINO cannot be more than five million (correct me if I'm wrong)

JEPA is just getting started

[Tenoke]

His main anti-LLM predictions have been consistently either wrong or misleading.

There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.

[onlyrealcuzzo]

His main LLM predictions have almost nothing to do with Arc AGI...

What exactly was he dead wrong about that is proven by any of this?

GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying.

He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3.

He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value.

Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.

[Tenoke]

>He's merely said they don't think

He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.

[onlyrealcuzzo]

> He said even 'GPT 5000' couldnt do things that they could do a month later, let alone by 5000.

What things specifically and when?

[Tenoke]

https://youtube.com/shorts/zQTt8TkcyfU?is=09r7XDqz2w6-Pygu

You probably wont like the edit but I dont have the timestamp of the original on hand, you can find it.

[onlyrealcuzzo]

That does not at all look cherry picked or taken out of context...

LeCun's ideas cannot be reduced to a 6 second clip...

You're missing the forrest for the trees, taking a singular example of a problem and thinking that if an LLM can solve the singular example it completely disproves LeCun is comical...

[cma]

> What exactly was he dead wrong about that is proven by any of this?

He said as you need more and more tokens models will fall apart because each additional token is a chance for a mistake and they will just exponentially fall apart. But in practice models have learned to identify and self-correct mistakes and if you look at the graphs more inference reasoning tokens almost always give far better accuracy.

[vatsachak]

That's true, but he's still correct, it's just that the context is now so large that only people using agent loops see "context rot"

His other criticism of LLMs that I like better is that they try to predict tokens instead of learned embeddings. Tokens are arbitrary and in order to decode LLMs you need technical analysis (see mechanistic interpretability).

With JEPA models so far, it seems that PCA on latent vectors suffices.

tldr: embeddings have a lot more room for improvement

[cma]

One of the big things that enabled extended context to work was training techniques for extending LLMs with reasoning, part of the thing he was saying wouldn't work.

[typon]

DinoV3 paper: https://arxiv.org/pdf/2508.10104#page=36

"we use a rough estimate of a total 9M GPU hours"

From CoreWeave, at current prices (~$2.46/hr spot to ~$6.16/hr on demand) would correspond to $22M–$55M.

The dataset is really where the cost is though - they used LVD-1689M - 1.6B images of curated web data from roughly 17B instagram images. This probably cost a huge amount of hours in human annotation, compute for algorithmic filtering, etc and not to mention probably a 20-50 person team working on this model.

You might want to change assumptions about how expensive these models are.

[vatsachak]

Thanks for the correction on the order of magnitude for the whole training process.

The 9M GPU hours includes the DINO v2 inference used in order to curate the data set.

The final training run used like 300000 dollars of compute.

Unfortunately we don't know how much RLVR + Agent training costs these companies. I'm just gonna say it's in the hundreds of millions, because they are supposedly making billions of profit on inference yet making billion dollar losses

[ainch]

Yann is a big SSL guy but I don't think he was involved in the original DINO - he's not listed as a co-author or anything.

[vatsachak]

DINO was created independent of JEPA but uses a similar principle of self supervised learning through minimizing the prediction error of a latent.

The difficulty in predicting a latent is so called "collapse"; the embedding neutral network can always output the zero vector and this would predict the output correctly.

There are different ways to solve this, DINO uses two different models - a teacher and a student and LeCunn uses an explicit term against collapsing to a single output.

Yann mentions DINO in his talks

[esafak]

ASI is going to be here by the time Lecun gets started.

[redactsureAI]

DINO is a transformer model?

[vatsachak]

JEPA can use a transformer, and DINO does so yes