Reward Collapse in Aligning Large Language Models
Ziang Song Tianle Cai Jason D. Lee Weijie J. Su
The extraordinary capabilities of large language models (LLMs) such as
ChatGPT and GPT-4 are in part unleashed by aligning them with reward models
that are trained on human preferences, which are often represented as rankings
of responses to prompts. In this paper, we document the phenomenon of
\textit{reward collapse}, an empirical observation where the prevailing
ranking-based approach results in an \textit{identical} reward distribution
\textit{regardless} of the prompts during the terminal phase of training. This
outcome is undesirable as open-ended prompts like ``write a short story about
your best friend'' should yield a continuous range of rewards for their
completions, while specific prompts like ``what is the capital of New Zealand''
should generate either high or low rewards. Our theoretical investigation
reveals that reward collapse is primarily due to the insufficiency of the
ranking-based objective function to incorporate prompt-related information
during optimization. This insight allows us to derive closed-form expressions
for the reward distribution associated with a set of utility functions in an
asymptotic regime. To overcome reward collapse, we introduce a prompt-aware
optimization scheme that provably admits a prompt-dependent reward distribution
within the interpolating regime. Our experimental results suggest that our
proposed prompt-aware utility functions significantly alleviate reward collapse
during the training of reward models.