  Minimum Bayes Risk (MBR) decoding can significantly improve translation
performance of Multilingual Large Language Models (MLLMs). However, MBR
decoding is computationally expensive. We show how the recently developed
Reinforcement Learning technique, Direct Preference Optimization (DPO), can
fine-tune MLLMs to get the gains of MBR without any additional computation in
inference. Our method uses only a small monolingual fine-tuning set and yields
significantly improved performance on multiple NMT test sets compared to MLLMs
without DPO.
