Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Reinforcement learning (RL) requires access to a reward function that incentivizes the right
behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL …

Learning a reward function from human preferences is challenging as it typically requires
having a high-fidelity simulator or using expensive and potentially unsafe actual physical …

Learning a reward function from human preferences is challenging as it typically requires
having a high-fidelity simulator or using expensive and potentially unsafe actual physical …

With the increasing deployment of artificial intelligence (AI) technologies, the potential of
humans working with AI agents has been growing at a great speed. Human-AI teaming is an …

There is an increasing interest in learning rewards and models of human intent from human
feedback. However, many methods use blackbox learning methods that, while expressive …

There is an increasing interest in learning reward functions that model human intent and
human preferences. However, many frameworks use blackbox learning methods that, while …

Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular
paradigm for capturing human intent to alleviate the challenges of hand-crafting the reward …

Sequential decisions and predictions are common problems in natural language
processing, robotics, and video games. Essentially, an agent interacts with an environment …

As AI systems become widely employed this technology will profoundly impact society. To
ensure this impact is positive it is essential to align these systems with the values and …