565 citations · 592 across the 10 of their papers we have counts for
5 papers · 1 filter
Prefix Sliding for efficient test-time scaling
Niklas Muennighoff, Zhengyang Wang, Zeyi Chen +15
Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models keep the entire reasoning tra…
Reusing Pre-Training Data at Test Time is a Compute Multiplier
Alex Fang, Thomas Voice, Ruoming Pang +2
Large language models learn from their vast pre-training corpora, gaining the ability to solve an ever increasing variety of tasks; yet although researchers work to improve these d…
Datasets, Documents, and Repetitions: The Practicalities of Unequal Data Quality
Alex Fang, Hadi Pouransari, Matt Jordan +4
Data filtering has become a powerful tool for improving model performance while reducing computational cost. However, as large language model compute budgets continue to grow, the…
Language models scale reliably with over-training and on downstream tasks
Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar +22
Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…