Comment by Moosdijk
2 days ago
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.
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)
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.
Thanks, this was helpful! Reading the seminal paper[0] on Universal Transformers also gave some insights:
> UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs.
Very interesting, it seems to be an “old” architecture that is only now being leveraged to a promising extent. Curious what made it an active area (with the works of Samsung and Sapient and now this one), perhaps diminishing returns on regular transformers?
0: https://arxiv.org/abs/1807.03819
> 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.
I meant going to the likeliest output (flash) or (iteratively) generating multiple outputs and (iteratively) choosing the best one (thinking/pro)
That's not how these models work.
Thinking models produce thinking tokens to reason out the answer.