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However, it\nhas limitations when the goal is for generating sequences of discrete tokens. A\nmajor reason lies in that the discrete outputs from the generative model make\nit difficult to pass the gradient update from the discriminative model to the\ngenerative model. Also, the discriminative model can only assess a complete\nsequence, while for a partially generated sequence, it is non-trivial to\nbalance its current score and the future one once the entire sequence has been\ngenerated. In this paper, we propose a sequence generation framework, called\nSeqGAN, to solve the problems. Modeling the data generator as a stochastic\npolicy in reinforcement learning (RL), SeqGAN bypasses the generator\ndifferentiation problem by directly performing gradient policy update. The RL\nreward signal comes from the GAN discriminator judged on a complete sequence,\nand is passed back to the intermediate state-action steps using Monte Carlo\nsearch. 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