RLHF-Blender: A Configurable Interactive Interface for Learning from
  Diverse Human Feedback
Yannick Metz David Lindner Raphaël Baur Daniel Keim Mennatallah El-Assady
To use reinforcement learning from human feedback (RLHF) in practical
applications, it is crucial to learn reward models from diverse sources of
human feedback and to consider human factors involved in providing feedback of
different types. However, the systematic study of learning from diverse types
of feedback is held back by limited standardized tooling available to
researchers. To bridge this gap, we propose RLHF-Blender, a configurable,
interactive interface for learning from human feedback. RLHF-Blender provides a
modular experimentation framework and implementation that enables researchers
to systematically investigate the properties and qualities of human feedback
for reward learning. The system facilitates the exploration of various feedback
types, including demonstrations, rankings, comparisons, and natural language
instructions, as well as studies considering the impact of human factors on
their effectiveness. We discuss a set of concrete research opportunities
enabled by RLHF-Blender. More information is available at
https://rlhfblender.info/.