2 citations · 2 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
HashAttention: Semantic Sparsity for Faster Inference
Aditya Desai, Shuo Yang, Alejandro Cuadron +3
Leveraging long contexts is crucial for advanced AI systems, but attention computation poses a scalability challenge. While scaled dot-product attention (SDPA) exhibits token spars…
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
Maximilian Böther, Abraham Sebastian, Pranjal Awasthi +2
Modern datasets span billions of samples, making training on all available data infeasible. Selecting a high quality subset helps in reducing training costs and enhancing model qua…
Modyn: Data-Centric Machine Learning Pipeline Orchestration
Maximilian Böther, Ties Robroek, Viktor Gsteiger +4
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential di…