Interesting paper to improve recurrent reasoning. Looped models are great because you get more reasoning out of a model without adding parameters. So this work proposes a looped architecture with a new training method. The authors report wins over prior looped models on five of six reasoning benchmarks. More details from the paper: Looped models reason by updating a hidden state again and again at inference time. The hard part is training. Gradients usually flow through only the last one…