The Human Factor: Addressing Diversity in Reinforcement Learning from Human Feedback

Abstract

Reinforcement Learning from Human Feedback (RLHF) is a promising approach to training agents to perform complex tasks by incorporating human feedback. However, the quality and diversity of this feedback can significantly impact the learning process. Humans are highly diverse in their preferences, expertise, and capabilities. This paper investigates the effects of conflicting feedback on the agent’s performance. We analyse the impact of environmental complexity and examine various query selection strategies. Our results show that RLHF performance rapidly degrades with even minimal conflicting feedback in simple environments, and current query selection strategies are ineffective in handling feedback diversity. We thus conclude that addressing diversity is crucial for RLHF, suggesting alternative reward modelling approaches are needed. Full code is available on GitHub.