Furong Huang

Reward models (RMs) trained from human preferences are central to aligning large language models, yet they often break under distribution shift or targeted perturbations. Existing failure discovery methods rely on prior knowledge of preference attributes and therefore do not scale to new models o...

Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning. A key cause is the model’s over-reliance on textual priors and underutilization of visual cues, leading to outputs th...

With Large Language Models (LLMs) rapidly approaching and potentially surpassing human-level performance, it has become imperative to develop approaches capable of effectively supervising and enhancing these powerful models using smaller, human-level models exposed to only human-level data. We ad...