Among all single models, LFT performs the best, followed by MinAvgOut. RL is also comparable with previous state-of-the-art models VHRED (attn) and Reranking-RL. We think that this is because LFT exerts no force in pulling the model predictions away from the ground-truth tokens, but rather just makes itself aware of how dull each response is. Consequently, its responses appear more relevant than the other two approaches. Moreover, the hybrid model (last row) outperforms all other models by a large margin.  
 Question: Which one of the four proposed models performed best?
the hybrid model MinAvgOut + RL