10 ms·
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
by Moosdijk 9mo 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.
- omneity 9mo agoIsn’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 9mo agoIt'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.
- omneity 9mo agoThanks, 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 https://arxiv.org/abs/1807.03819
- nl 9mo ago> 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.
- deleted 9mo ago[deleted]