5 papers
Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training
Jingwei Zuo, Cong Zeng, Ilyas Chahed +6
The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve…
Grouped Query Experts: Mixture-of-Experts on GQA Self-Attention
Vishesh Tripathi, Abhay Kumar
Self-attention is central to Transformer performance and is often the most expensive part of the Transformer at long context lengths because its pairwise token interactions scale q…
ZClip: Adaptive Spike Mitigation for LLM Pre-Training
Abhay Kumar, Louis Owen, Nilabhra Roy Chowdhury +1
Training large language models (LLMs) presents numerous challenges, including gradient instability and loss spikes. These phenomena can lead to catastrophic divergence, requiring c…
A Refined Analysis of Massive Activations in LLMs
Louis Owen, Nilabhra Roy Chowdhury, Abhay Kumar +1
Motivated in part by their relevance for low-precision training and quantization, massive activations in large language models (LLMs) have recently emerged as a topic of interest.…
Variance Control via Weight Rescaling in LLM Pre-training
Louis Owen, Abhay Kumar, Nilabhra Roy Chowdhury +1
The outcome of Large Language Model (LLM) pre-training strongly depends on weight initialization and variance control strategies. Although the importance of initial variance contro…