The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to prompt and more capable in complex settings. RLHF at its core is providing a new toolkit to optimize LLMs other than next-token prediction, enabling the integration of qualitative training goals. The attempted match between user preferences and downstream performance, which happens in a learned reward model, results in an optimization landscape where training and evaluation metrics can appear correlated. The apparent correlation can lead to unexpected behaviors and stories of "too much RLHF." In RLHF, challenges emerge because the following sub-modules are not consistent with each other: the reward model training, the policy model training, and the policy model evaluation. This mismatch results in models that sometimes avoid user requests for false safety flags, are difficult to steer to an intended characteristic, or always answer in a specific style. As chat model evaluation becomes increasingly nuanced, the reliance on a perceived link between reward model score and downstream performance drives the objective mismatch issue. In this paper, we illustrate the cause of this issue, reviewing relevant literature from model-based reinforcement learning, and discuss relevant solutions to encourage further research. By solving objective mismatch in RLHF, the LLMs of the future will be more precisely aligned to user instructions for both safety and helpfulness.