Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- Anyone remember Universal Transformers paper (Dehghani et al) from back in 2018? Recurrent transformers have a history as long as transformers themselves.
Somewhat unclear how particularly novel this is vs a way to save compute.
by nighthawk454 - So OpenAI’s stance on interpretability (ai safety) is now basically that Blues Brothers meme: two guys in dark sunglasses, driving at night in a car with a broken windshield, pedal to the metal, asking, "What could possibly go wrong ?"by mentalgear
- Seems conceptually connected to the "repeat yourself" hack that improves models by duplicating layers: https://dnhkng.github.io/posts/rys/by pu_pe
- Related ongoing thread:
OpenAI's new reasoning technique alarms AI safety experts - https://news.ycombinator.com/item?id=49552395
by dang - I was under the impression that intermediate tokens (“chain of thought”) are _not_ a representation of a model’s logical path, with one study observing that you can replace intermediate tokens with single character chains and still get the increased precision…by khalic
- >In contrast to a classic RNN, there's no unbounded hidden state accumulating across an entire trajectory
I don' understand this line. In a classic RNN hidden state is bounded dimension. In fact it's transformers that technically have unbounded hidden state.
You can't parallelize classic nonlinear RNNs for various reasons but in training both RNN and Transformer depend on the entire sequence history in a way that is unbounded. Of course in practice you just train on a max sequence length.
RNN xhat[t+1]=f(x[t],h[t])
Transformer/self-attention xhat[t+1]=f(x[t],h[t],h[t-1],...,h[1])
by kjshsh123 - Sebastian Raschka posted about this architecture:
> A lot of hype around OpenAI's Astra model here on my timeline today. Apparently, this goes back to a new article from The Information, which said Astra is a "recurrent depth or looped transformer".
> It's always interesting to read about new or different approaches (including rumors about what the closed labs may be up to), but let's debunk this a bit.
> About 2 months ago, I shared the architecture details of Nanbeige, for example, where "Nanbeige4.2-3B is pretrained from scratch on 28T tokens with a Looped Transformer that reuses the layer stack to increase capacity without adding parameters."
> Yes, that's it. The looped transformer idea is just reusing layers in the transformer block.
> In the case of Nanbeige, the main idea is to reuse the same 22-layer stack (=transformer block) twice instead of once. So, effectively it extends the 22-layer architecture to 44 layers, but without duplicating the weights.
> In simple terms, this roughly doubles the size of the model (if we ignore the embedding and output layers for a second). But instead of requiring 2x the storage and RAM to host this model, it stays at the same size since we reuse the components. However, it's almost 2x as expensive in terms of compute, because we run the embedded text through almost 2x as many layers.
> Why? In the Nanbeige 4.2 technical report, the researchers found that two passes gave the best trade-off and retained about 75% of the token efficiency of a standard architecture. (More passes gave barely any gains but made the training much slower and much more expensive.)
> While, as far as I know, Nanbeige 4.2 is the first notable open-weight model that adopted this approach, the idea goes back to the NeurIPS paper "Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation". Actually, this paper proposes a mechanism that is a bit more sophisticated by adding a learned router that determines whether each token receives one, two, or more passes. So, easy tokens can exit early while harder tokens receive additional computation.
> In sum, Astra may be a really good model, but this shouldn't be about this "looped transformer aspect," which is just a tiny architectural tweak.
by anon373839 - I like the idea of more reccurance in the transformer level. Chain of thought always seemed so clunky. Its just not the way the human brain processes information. Its an extrmeely crude approximation at bestby samrus