Were RNNs all we needed?

It's curse and a blessing that discussion of topics happens in so many different places. I found this comment on Twitter/X interesting: https://x.com/fchollet/status/1841902521717293273

"Interesting work on reviving RNNs. https://arxiv.org/abs/2410.01201 -- in general the fact that there are many recent architectures coming from different directions that roughly match Transformers is proof that architectures aren't fundamentally important in the curve-fitting paradigm (aka deep learning)

Curve-fitting is about embedding a dataset on a curve. The critical factor is the dataset, not the specific hard-coded bells and whistles that constrain the curve's shape. As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime."

Not always, I think?

Opinions probably differ, for example, on John Backus's paper "Can programming be liberated from the Von Neumann style?" Many fans of functional programming would say the answer is yes, but Backus himself expressed less enthusiasm in interviews later in his life.

I think the important point, though, is that academic papers and newspaper articles are not the same, and titles in the form of questions function differently in the two domains. Journalists tend to use titles like these to dissemble and sensationalize. When academics use these kinds of titles for peer-reviewed articles, it's because they really are asking an honest question. Backus was doing it in his paper. The authors of this paper are doing the same. They end the paper by re-iterating the question before launching into a discussion of the limitations that prevent them from reaching any firm conclusions on the answer to this question.

> "As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime."

I haven't fully ingested the paper yet, but it looks like it's focused more on compute optimization than the size of the dataset:

> ... and (2) are fully parallelizable during training (175x faster for a sequence of length 512

Even if many types of architectures converge to the same loss over time, finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale.

The name of the paper contrasts with the paper that spawned Transformer architecture, which itself is a reference to the song "All You Need Is Love" by the Beatles. https://en.wikipedia.org/wiki/Attention_Is_All_You_Need

The model in the paper isn't a "real" RNN due making it parallelizable, for same the reasons described in https://arxiv.org/abs/2404.08819 , and hence is theoretically less powerful than a "real" RNN (struggles at some classes of problems that RNNs traditionally excel at). On the other hand, https://arxiv.org/abs/2405.04517 contains a "real" RNN component, which demonstrates a significant improvement on the kind of state-tracking problems that transformers struggle with.