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 …

Large language models (LLMs) often contain misleading content, emphasizing the need to
align them with human values to ensure secure AI systems. Reinforcement learning from …

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) facilitates the alignment of large
language models with human preferences, significantly enhancing the quality of interactions …

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense
potential to catalyze societal advancement, yet their deployment is often impeded by …

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 proven effective in aligning large
language models (LLMs) with human preferences. However, gathering high-quality human …

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 …

Aligning large language models (LLMs) with human preferences through reinforcement
learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward …

Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm
for aligning large language models (LLMs) with human preferences. Typically, RLHF …