Loop the Loopies

Researchers have introduced the Loopie series, two Mixture-of-Experts models (20B parameters with 2B active and 6B with 0.6B active) that outperform vanilla Transformer baselines trained with the same compute budget, addressing the long-standing challenge that increasing parameter count outperforms looping. A novel post-training method gives Loopie frontier-level reasoning abilities.

Computer Science Computation and Language Submitted on 17 Jul 2026 v1 https://arxiv.org/abs/2607.16051v1 , last revised 20 Jul 2026 this version, v2 Title:Loop the Loopies View PDF /pdf/2607.16051 HTML experimental https://arxiv.org/html/2607.16051v2 Abstract:We present the Loopie series, consisting of two Mixture-of-Experts MoE models: a 20B-parameter model with 2B active parameters and a 6B-parameter model with 0.6B active parameters. Looped Transformers have long faced a challenge: given an N times increase in pre-training compute, increasing the parameter count by a factor of N usually outperforms looping a model N times. Loopie addresses this challenge. Extensive ablation studies, including comparisons with a vanilla 30B-A3B model, show that Loopie substantially outperforms vanilla Transformer baselines trained with the same compute budget. With a novel post-training method, Loopie develops strong reasoning abilities and achieves frontier-level reasoning performance. Submission history From: Zitian Gao view email /show-email/52340bf5/2607.16051 Fri, 17 Jul 2026 15:28:43 UTC 829 KB v1 /abs/2607.16051v1 v2 Mon, 20 Jul 2026 15:59:50 UTC 835 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .