[thomasahle]

> Latent / embedding-space reasoning seems a step in the right direction

Might be good for reasoning, but it's terrible for interpretation / AI-safety.

[Tostino]

Why is it any different to do 4 recurrent passes than having a model that is 4x deeper?

[lonk11]

Running one layer 4 times should fetch the weights of that layer once. Running 4 layers makes you fetch 4x parameters.

The recurrent approach is more efficient when memory bandwidth is the bottleneck. They talk about it in the paper.

[Tostino]

Yeah, understood. I'm excited for the reduction in parameter count that will come when this is taken up in major models.

I meant it rhetorically in reference to interpretability. I don't see a real difference between training a model that is 100b parameters vs a (fixed) 4x recurrent 25b parameter model as far as understanding what the model is `thinking` for the next token prediction task.

You should be able to use the same interpretability tooling for either. It can only `scheme` so much before it outputs the next token no matter if the model is just a fixed size and quite deep, or recurrent.

[thomasahle]

I guess the most interpretable is to have as shallow a model as possible, but with longer cot. It would be quite interesting seeing the trade-off between the two. Though, unfortunately, deeper is probably better.