Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Reinforcement learning from human feedback (RLHF) is an effective technique for aligning
large language models (LLMs) to human preferences, but gathering high-quality human …

Language Models (LMs) often cannot be deployed because of their potential to harm users
in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using …

Learning from human feedback has been shown to be effective at aligning language models
with human preferences. Past work has often relied on Reinforcement Learning from Human …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Recent large language models often answer factual questions correctly. But users can't trust
any given claim a model makes without fact-checking, because language models can …

As language models become more powerful, training and evaluation are increasingly
bottlenecked by the data and metrics used for a particular task. For example, summarization …

Many modern approaches to offline Reinforcement Learning (RL) utilize behavior
regularization, typically augmenting a model-free actor critic algorithm with a penalty …

Reward learning enables the application of reinforcement learning (RL) to tasks where
reward is defined by human judgment, building a model of reward by asking humans …