16 papers
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +34
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities
Christopher Zanoli, Andrea Giovannini, Tengjun Jin +2
Constructing Extract-Load-Transform (ELT) pipelines is a labor-intensive data engineering task and a high-impact target for AI automation. On ELT-Bench, the first benchmark for end…
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…
Understanding GPU Resource Interference One Level Deeper
Paul Elvinger, Foteini Strati, Natalie Enright Jerger +1
GPUs are vastly underutilized, even when running resource-intensive AI applications, as GPU kernels within each job have diverse resource profiles that may saturate some parts of a…
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…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…