106 citations · 166 across the 25 of their papers we have counts for
3 papers · 2 filters
Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter
Qinghao Hu, Shang Yang, Junxian Guo +7
The emergence of Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving. However, trainin…
Semantic-Aware Scheduling for GPU Clusters with Large Language Models
Zerui Wang, Qinghao Hu, Ana Klimovic +4
Deep learning (DL) schedulers are pivotal in optimizing resource allocation in GPU clusters, but operate with a critical limitation: they are largely blind to the semantic context…
Mixtera: A Data Plane for Foundation Model Training
Maximilian Böther, Xiaozhe Yao, Tolga Kerimoglu +3
State-of-the-art large language and vision models are trained over trillions of tokens that are aggregated from a large variety of sources. As training data collections grow, manua…