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To use reinforcement learning from human feedback (RLHF) in practicalapplications, it is crucial to learn reward models from diverse sources ofhuman feedback and to consider human factors involved in providing feedback ofdifferent types. However, the systematic study of learning from diverse typesof feedback is held back by limited standardized tooling available toresearchers. To bridge this gap, we propose RLHF-Blender, a configurable,interactive interface for learning from human feedback. RLHF-Blender provides amodular experimentation framework and implementation that enables researchersto systematically investigate the properties and qualities of human feedbackfor reward learning. The system facilitates the exploration of various feedbacktypes, including demonstrations, rankings, comparisons, and natural languageinstructions, as well as studies considering the impact of human factors ontheir effectiveness. We discuss a set of concrete research opportunitiesenabled by RLHF-Blender. More information is available athttps://rlhfblender.info/.

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback