arXiv paper examines looped models that update hidden states recurrently at inference

A new preprint discusses looped neural architectures that improve problem solving by repeatedly updating an internal hidden state during inference rather than scaling parameters. The authors note that training such models is challenging because backpropagation through repeated steps is costly. The work explores how these iteration-based approaches relate to spending more compute at test time.