UpLoop: Upcycling Large Language Models with Gated Loop

Tieyuan Chen, Huabin Liu, Yingtao Zhang, Shijie Li, Tengyu Pan +2

Looped large language models trade parameter storage for recurrent computation, but most are trained from scratch. In practice, pretraining with looped depth is expensive, making post-training upcycling of a pretrained non-looped LLM an attractive alternative. We introduce \textbf{UpLoop}, a post-training recipe based on \emph{layer-wise} recurrence. Instead of looping a whole model or a middle block, UpLoop loops each selected layer locally, preserving the pretrained depth hierarchy and avoiding feature-space mismatch. To make loop execution useful, we add hidden-wise gates on the attention and MLP residual branches, so later loops act as progressive refinements rather than redundant rewrites. Empirically, UpLoop breaks the commonly observed saturation at two loops ($R=2$) in prior looped models. Starting from Qwen3-4B Base and applying SFT, UpLoop improves the average accuracy across 20 benchmarks by 6.17\% at $R=3$. We use $R=3$ as the main setting for the best accuracy--cost trade-off, while both native and extrapolated $R=4$ yield further gains.