4.3k citations · 15.5k across the 33 of their papers we have counts for
39 papers
Let's Verify Step by Step
Hunter Lightman, Vineet Kosaraju, Yura Burda +7
In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce…
GPT-4 Technical Report
OpenAI, Josh Achiam, Steven Adler +278
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-wor…
Scaling laws for single-agent reinforcement learning
Jacob Hilton, Jie Tang, John Schulman
Recent work has shown that, in generative modeling, cross-entropy loss improves smoothly with model size and training compute, following a power law plus constant scaling law. One…
Scaling Laws for Reward Model Overoptimization
Leo Gao, John Schulman, Jacob Hilton
In reinforcement learning from human feedback, it is common to optimize against a reward model trained to predict human preferences. Because the reward model is an imperfect proxy,…
Efficient Training of Language Models to Fill in the Middle
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak +4
We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the midd…
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang +17
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic,…