A Survey of Reinforcement Learning from Human Feedback

Timo Kaufmann, Paul Weng, Viktor Bengs et al.

2023 · arXiv (Cornell University) · 34 citations

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. Building on prior work on the related setting of preference-based reinforcement learning (PbRL), it stands at the intersection of artificial intelligence and human-computer interaction. This positioning provides a promising approach to enhance the performance and adaptability of intelligent systems while also improving the alignment of their objectives with human values. The success in training large language models…

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