Abstract Generative Artificial Intelligence (AI) is one of the most exciting developments in
Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has …

The ability to collect a large dataset of human preferences from text-to-image users is
usually limited to companies, making such datasets inaccessible to the public. To address …

Learning from human feedback has been shown to improve text-to-image models. These
techniques first learn a reward function that captures what humans care about in the task …

The stunning qualitative improvement of text-to-image models has led to their widespread
attention and adoption. However, we lack a comprehensive quantitative understanding of …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

Large language models (LLMs) are fine-tuned using human comparison data with
Reinforcement Learning from Human Feedback (RLHF) methods to make them better …

Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize
NeRF. Notably, these methods are able to produce high-quality 3D scenes without training …

Incorporating human feedback has been shown to be crucial to align text generated by large
language models to human preferences. We hypothesize that state-of-the-art instructional …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Recent years have seen a surge in the popularity of commercial AI products based on
generative, multi-purpose AI systems promising a unified approach to building machine …