1 citations · 1 across the 8 of their papers we have counts for
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MuonBP: Faster Muon via Block-Periodic Orthogonalization
Ahmed Khaled, Kaan Ozkara, Tao Yu +2
Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan, Jin, et al., 2024) combines gradient orthogona…
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
Song Bian, Tao Yu, Shivaram Venkataraman +1
Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models gro…
Stochastic Rounding for LLM Training: Theory and Practice
Kaan Ozkara, Tao Yu, Youngsuk Park
As the parameters of Large Language Models (LLMs) have scaled to hundreds of billions, the demand for efficient training methods -- balancing faster computation and reduced memory…
Training LLMs with MXFP4
Albert Tseng, Tao Yu, Youngsuk Park
Low precision (LP) datatypes such as MXFP4 can accelerate matrix multiplications (GEMMs) and reduce training costs. However, directly using MXFP4 instead of BF16 during training si…