Researchers have proposed a new type of feed-forward neural network called TriPLU for use in tiny language models. Instead of using the usual gated FFN branch, TriPLU uses a product-only degree-3 branch that multiplies three projected streams coordinatewise. In experiments on small datasets, TriPLU showed improved performance compared to other methods, particularly at low learning rates. However, the authors caution that this improvement is specific to certain regimes and does not necessarily translate to larger models or more complex tasks. --- Why it matters: This matters because it shows a potential way to improve the efficiency of tiny language models, which are used in many real-world applications such as chatbots and virtual assistants. By reducing the computational requirements of these models, developers may be able to deploy them on lower-power devices or with reduced latency. Source: https://arxiv.org/abs/2608.20360