核心概念
Reinforcement learning from human feedback (RLHF) is a powerful approach that learns agent behavior by incorporating interactive human feedback, overcoming the limitations of manually engineered reward functions.
摘要
This survey provides a comprehensive overview of the fundamentals and recent advancements in reinforcement learning from human feedback (RLHF).
Key highlights:
RLHF addresses the challenges of reward engineering in standard reinforcement learning by learning the agent's objective from human feedback instead of a predefined reward function. This can enhance the performance, adaptability, and alignment of intelligent systems with human values.
The survey covers the core components of RLHF: feedback types, label collection, reward model training, and policy learning. It examines the intricate dynamics between RL agents and human input, shedding light on the symbiotic relationship between algorithms and human feedback.
Recent methodological developments are discussed, including fusing multiple feedback types, enhancing query efficiency through active learning, incorporating psychological insights to improve feedback quality, and using meta-learning and semi-supervised techniques to adapt learned preferences.
Theoretical insights into RLHF are provided, offering new perspectives on policy learning, the relationship between preference-based and reward-based learning, and Nash learning from human feedback.
The survey also covers a wide range of RLHF applications, supporting libraries, benchmarks, and evaluation approaches, providing researchers and practitioners with a comprehensive understanding of this rapidly growing field.
統計資料
"Reinforcement learning from human feedback (RLHF) stands at the intersection of artificial intelligence and human-computer interaction, offering a promising avenue to enhance the performance and adaptability of intelligent systems while also improving the alignment of their objectives with human values."
"Recent focus has been on RLHF for large language models (LLMs), where RLHF played a decisive role in directing the model's capabilities toward human objectives."
引述
"RLHF differs from RL in that the objective is defined and iteratively refined by the human in the loop instead of being specified ahead of time."
"RLHF not only has the potential to overcome the limitations and issues of classical RL methods but also has potential benefits for agent alignment, where the agent's learning goals are more closely aligned with human values, promoting ethically sound and socially responsible AI systems."