Xingyu Qu

2 accepted papers

2026

Can Muon Fine-tune Adam-Pretrained Models?

Xingyu Qu, Peigeng Huang, Samuel Horváth

ICML 2026poster

Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are pretrained with Adam, and naively switching to Muon for fine-tuning leads to degraded performance due to an optimizer mismatch. We study this misma…

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2022

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer

Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu

NeurIPS 2022accept

Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a key role in SPL but where to optimally terminate this process…

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