[2607.16051] Loop the Loopies!

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Computer Science > Computation and Language

arXiv:2607.16051 (cs)

[Submitted on 17 Jul 2026 (v1), last revised 20 Jul 2026 (this version, v2)]

Title:Loop the Loopies!

Authors:Zitian Gao, Yilong Chen, Yihao Xiao, Xinyu Yang, Ran Tao, Joey Zhou, Bryan Dai<br>View a PDF of the paper titled Loop the Loopies!, by Zitian Gao and 6 other authors

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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.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.16051 [cs.CL]

(or<br>arXiv:2607.16051v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.16051

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arXiv-issued DOI via DataCite

Submission history<br>From: Zitian Gao [view email]<br>[v1]<br>Fri, 17 Jul 2026 15:28:43 UTC (829 KB)

[v2]<br>Mon, 20 Jul 2026 15:59:50 UTC (835 KB)

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