@aibutsimple: If you take a transformer block and stack it 20 times, you end up with the popular transformer architecture. If you make it output tokens, it redoes the math for every earlier token at each step, so you save that math in a KV cache, and you get fast autoregressive generation. But that cache grows with every token, so you swap most of the attention for linear attention. This squeezes the history into a fixed-size summary, so you get what we call a transformer hybrid. Now context is cheap, but each token only gets as many processing steps as there are layers. You could stack 1,000 layers, but every layer brings its own weights, and memory explodes. So you take the output of a layer and feed it through the same layer, over and over. Every pass costs compute, but you never add new weights. You have now made a looped transformer, a popular LLM architectural tweak used in models like GPT-6 Astra. A simple idea from 2018, now one of the hottest architecture tweaks in AI. If this was interesting, we explain popular AI research simply in our weekly newsletter. Follow @aibutsimple for more frontier research explanations. #deeplearning #machinelearning #research #coding

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Saturday 31 October 2026 13:23:01 GMT