6.3k citations
- University of California, BerkeleyUS18 papers
- Google (United States)US11 papers
- Stanford UniversityUS8 papers
- Carnegie Mellon UniversityUS6 papers
- Berkeley CollegeUS4 papers
- Uber AI (United States)US4 papers
- ETH ZurichCH3 papers
- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- McGill UniversityCA3 papers
- Microsoft (United States)US3 papers
- Mila - Quebec Artificial Intelligence InstituteCA3 papers
- The University of Texas at AustinUS3 papers
11 papers · 1 filter
Filling gaps in trustworthy development of AI
Shahar Avin, Haydn Belfield, Miles Brundage +9
The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address t…
Batch size-invariance for policy optimization
Jacob Hilton, Karl Cobbe, John Schulman
We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters. Stochastic gradient descent is well-kno…
Recursively Summarizing Books with Human Feedback
Jeff Wu, Long Ouyang, Daniel M. Ziegler +4
A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this pro…
MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Kunhao Zheng, Jesse Michael Han, Stanislas Polu
We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
Irene Solaiman, Christy Dennison
Language models can generate harmful and biased outputs and exhibit undesirable behavior according to a given cultural context. We propose a Process for Adapting Language Models to…