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arXiv · 2604.24073

FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost

Chenhao Feng·Haoli Zhang·Shakhzod Ali-Zade·Yanli Zhao·Liang Luo·Jennifer Cao·Lisen Deng·Siqiao Chen·Chenyu Zhao·Tristan Rice·Daniel Johnson·Min Si·Tiantu Xu·Yi Zhang·Siqi Yan·Chuanhao Zhuge·Min Ni·Bi Xue·Qunshu Zhang·Shen Li
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Abstract

Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent heterogeneity in data characteristics frequently result in substantial under-utilization of computational resources during large-scale training, primarily due to computational bubbles caused by severe stragglers and slow blocking communications. This paper introduces FreeScale, a solution designed to (1) mitigate the straggler problem through meticulously load balanced input samples (2) minimize the blocking communication by overlapping prioritized embedding communications with computations (3) resolve the GPU resource competition during computation and communication overlapping by communicating through SM-Free techniques. Empirical evaluation demonstrates that FreeScale achieves up to 90.3% reduction in computational bubbles when applied to real-world workloads running on 256 H100 GPUs.

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Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade, Yanli Zhao, Liang Luo, Jennifer Cao, Lisen Deng, Siqiao Chen, Chenyu Zhao, Tristan Rice, Daniel Johnson, Min Si, Tiantu Xu, Yi Zhang, Siqi Yan, Chuanhao Zhuge, Min Ni, Bi Xue, Qunshu Zhang, Shen Li. 2026-04-27. FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost. https://arxiv.org/abs/2604.24073

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