9 papers
BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models
Shengao Wang, Wenqi Wang, Zecheng Wang +20
Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded…
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining
Jeffrey Li, Josh Gardner, Doug Kang +10
One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing…
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…
SDS -- See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration
Maria Stamatopoulou, Jeffrey Li, Dimitrios Kanoulas
Imagine a robot learning locomotion skills from any single video, without labels or reward engineering. We introduce SDS ("See it. Do it. Sorted."), an automated pipeline for skill…
Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice +7
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies…
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh +8
Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes availab…