  Reinforcement learning from human feedback (RLHF) plays a crucial role in
aligning language models with human preferences. While the significance of
dataset quality is generally recognized, explicit investigations into its
impact within the RLHF framework, to our knowledge, have been limited. This
paper addresses the issue of text quality within the preference dataset by
focusing on direct preference optimization (DPO), an increasingly adopted
reward-model-free RLHF method. We confirm that text quality significantly
influences the performance of models optimized with DPO more than those
optimized with reward-model-based RLHF. Building on this new insight, we
propose an extension of DPO, termed filtered direct preference optimization
(fDPO). fDPO uses a trained reward model to monitor the quality of texts within
the preference dataset during DPO training. Samples of lower quality are
discarded based on comparisons with texts generated by the model being
optimized, resulting in a more accurate dataset. Experimental results
demonstrate that fDPO enhances the final model performance. Our code is
available at https://github.com/CyberAgentAILab/filtered-dpo.
