2 papers
cs.LG2026
BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
Zili Zhang, Chengxu Yang, Shenglong Zhang +8
Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, b…
cs.AI2025
SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
Yuliang Liu, Guohao Wu, Shenglong Zhang +4
The efficient distributed training of Large Language Models (LLMs) is severely hampered by the extreme variance in context lengths. This data heterogeneity, amplified by convention…