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arXiv Machine Learning By Yanggan Gu, Yuanyi Wang, Zhen Li, Shuo Cai, Yuhang Liu, Junzhuo Li, Zihao Wang, Hongxia Yang

SMAT: Simple and Efficient Merge-Aware Training

Read the original on arXiv Machine Learning →

SMAT (Simple and Efficient Merge-Aware Training) is a method that jointly optimizes expert loss and expected loss at simulated merged parameters, addressing the shortcomings of standard expert training for model merging. It models common merging operations—Scale, Mask, and Perturb—by sampling scaling coefficients, masks, and additive noise, and introduces efficient techniques such as periodic scheduling, kernel fusion, and parameter storage switching to keep training overhead minimal. Across multiple language and vision-language backbones, SMAT improves mean scores by 1.07–2.16 points over the strongest baseline while adding less than 2% training-time overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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