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[ARTICLE · art-67045] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

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.

read2 min views1 publishedJul 21, 2026
Loop the Loopies
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[Submitted on 17 Jul 2026 (

[v1](https://arxiv.org/abs/2607.16051v1)), last revised 20 Jul 2026 (this version, v2)]# Title:Loop the Loopies!

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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.

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)

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