[Moosdijk]

Interesting. Instead of running the model once (flash) or multiple times (thinking/pro) in its entirety, this approach seems to apply the same principle within one run, looping back internally.

Instead of big models that “brute force” the right answer by knowing a lot of possible outcomes, this model seems to come to results with less knowledge but more wisdom.

Kind of like having a database of most possible frames in a video game and blending between them instead of rendering the scene.

[omneity]

Isn’t this in a sense an RNN built out of a slice of an LLM? Which if true means it might have the same drawbacks, namely slowness to train but also benefits such as an endless context window (in theory)

[ctoa]

It's sort of an RNN, but it's also basically a transformer with shared layer weights. Each step is equivalent to one transformer layer, the computation for n steps is the same as the computation for a transformer with n layers.

The notion of context window applies to the sequence, it doesn't really affect that, each iteration sees and attends over the whole sequence.

[nl]

> Instead of running the model once (flash) or multiple times (thinking/pro) in its entirety

I'm not sure what you mean here, but there isn't a difference in the number of times a model runs during inference.

[Moosdijk]

I meant going to the likeliest output (flash) or (iteratively) generating multiple outputs and (iteratively) choosing the best one (thinking/pro)