Aligning language models (LMs) to human preferences has emerged as a critical pursuit,
enabling these models to better serve diverse user needs. Existing methods primarily focus …

Diffusion models have emerged as the leading paradigm in generative modeling, excelling
in various applications. Despite their success, these models often misalign with human …

Reinforcement Learning from Human Feedback (RLHF) has been commonly used to align
the behaviors of Large Language Models (LLMs) with human preferences. Recently, a …

Because it is difficult to precisely specify complex objectives, reinforcement learning policies
are often optimized using proxy reward functions that only approximate the true goal …

In reinforcement learning, specifying reward functions that capture the intended task can be
very challenging. Reward learning aims to address this issue by learning the reward …

Large language models, particularly those based on BERT, have shown notable
performance in various natural language processing tasks. This study focuses on comparing …