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 …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to
optimize downstream reward functions. While diffusion models are widely known to provide …

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 …

We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-
tuning diffusion models to maximize differentiable reward functions, such as scores from …

Using reinforcement learning with human feedback (RLHF) has shown significant promise in
fine-tuning diffusion models. Previous methods start by training a reward model that aligns …

Reinforcement Learning From Human Feedback (RLHF) has been a critical to the success
of the latest generation of generative AI models. In response to the complex nature of the …

Diffusion-based text-to-image generative models eg Stable Diffusion have revolutionized the
field of content generation enabling significant advancements in areas like image editing …

Diffusion models have emerged as the de facto paradigm for video generation. However
their reliance on web-scale data of varied quality often yields results that are visually …