Towards sequence level optimization, previous works (Ranzato et al., 2016;Paulus et al., 2018) employ reinforcement learning (RL) with a policy-gradient approach which works around the difficulty of differentiating the reward function by using it as a weight.
rEINFOrCE is known to suffer from high sample variance and credit assignment problems which makes the training process difficult and unstable besides resulting in models that are hard to reproduce (Henderson et al., 2018).