  Foundation models, specifically Large Language Models (LLMs), have lately
gained wide-spread attention and adoption. Reinforcement Learning with Human
Feedback (RLHF) involves training a reward model to capture desired behaviors,
which is then used to align LLM's. These reward models are additionally used at
inference-time to estimate LLM responses' adherence to those desired behaviors.
However, there is little work measuring how robust these reward models are to
distribution shifts. In this work, we evaluate how reward model performance -
measured via accuracy and calibration (i.e. alignment between accuracy and
confidence) - is affected by distribution shift. We show novel calibration
patterns and accuracy drops due to OOD prompts and responses, and that the
reward model is more sensitive to shifts in responses than prompts.
Additionally, we adapt an OOD detection technique commonly used in
classification to the reward model setting to detect these distribution shifts
in prompts and responses.
