Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

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 …

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large
language models (LLMs) with human preferences. However, gathering high-quality human …

With the development of large language models (LLMs), striking a balance between the
performance and safety of AI systems has never been more critical. However, the inherent …

Reinforcement learning from human feedback (RLHF) has become a pivot technique in
aligning large language models (LLMs) with human preferences. In RLHF practice …

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is
increasingly treated as a crucial ingredient for high performance large language …

This study is an empirical investigation into the semantic vulnerabilities of four popular pre-
trained commercial Large Language Models (LLMs) to ideological manipulation. Using …

Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large
language models with human preferences, significantly enhancing the quality of interactions …

Large language models (LLMs) have formulated a blueprint for the advancement of artificial
general intelligence. Its primary objective is to function as a human-centric (helpful, honest …

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology
for aligning language models with human values and intentions, enabling models to …