  Faithfulness, expressiveness, and elegance is the constant pursuit in machine
translation. However, traditional metrics like \textit{BLEU} do not strictly
align with human preference of translation quality. In this paper, we explore
leveraging reinforcement learning with human feedback (\textit{RLHF}) to
improve translation quality. It is non-trivial to collect a large high-quality
dataset of human comparisons between translations, especially for low-resource
languages. To address this issue, we propose a cost-effective preference
learning strategy, optimizing reward models by distinguishing between human and
machine translations. In this manner, the reward model learns the deficiencies
of machine translation compared to human and guides subsequent improvements in
machine translation. Experimental results demonstrate that \textit{RLHF} can
effectively enhance translation quality and this improvement benefits other
translation directions not trained with \textit{RLHF}. Further analysis
indicates that the model's language capabilities play a crucial role in
preference learning. A reward model with strong language capabilities can more
sensitively learn the subtle differences in translation quality and align
better with real human translation preferences.
