  We argue for the epistemic and ethical advantages of pluralism in
Reinforcement Learning from Human Feedback (RLHF) in the context of Large
Language Models (LLM). Drawing on social epistemology and pluralist philosophy
of science, we suggest ways in which RHLF can be made more responsive to human
needs and how we can address challenges along the way. The paper concludes with
an agenda for change, i.e. concrete, actionable steps to improve LLM
development.
