391 citations · 391 across the 3 of their papers we have counts for
6 papers
DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments
Saurav Kadavath, Samuel Paradis, Jacob Yeung
Histone modifications play a critical role in gene regulation. Consequently, predicting gene expression from histone modification signals is a highly motivated problem in epigeneti…
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Yuntao Bai, Andy Jones, Kamal Ndousse +28
We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment tra…
Pretraining & Reinforcement Learning: Sharpening the Axe Before Cutting the Tree
Saurav Kadavath, Samuel Paradis, Brian Yao
Pretraining is a common technique in deep learning for increasing performance and reducing training time, with promising experimental results in deep reinforcement learning (RL). H…
Measuring Coding Challenge Competence With APPS
Dan Hendrycks, Steven Basart, Saurav Kadavath +8
While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importan…
Measuring Mathematical Problem Solving With the MATH Dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath +5
Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, w…
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath +1
Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often n…