SeqGAN is a sequence generation framework that addresses the limitations of traditional generative adversarial networks (GANs) in generating discrete token sequences. Unlike conventional GANs, which struggle with discrete outputs due to the difficulty of gradient backpropagation, SeqGAN models the data generator as a stochastic policy in reinforcement learning (RL), enabling direct gradient policy updates. The RL reward signal comes from the GAN discriminator, which evaluates the generated sequence and provides feedback through Monte Carlo search. This approach allows for effective training of the generative model by balancing immediate and future rewards. The paper proposes SeqGAN as a solution to the challenges of generating discrete sequences, where the generator is treated as an RL agent. The state is the sequence generated so far, and the action is the next token to be generated. The discriminative model evaluates the sequence and provides feedback to guide the learning of the generative model. To handle the issue of gradient backpropagation in discrete data, the generative model is treated as a stochastic parametrized policy, and Monte Carlo search is used to approximate the state-action value function. Extensive experiments on synthetic and real-world tasks demonstrate that SeqGAN significantly outperforms strong baselines, including maximum likelihood methods, scheduled sampling, and PG-BLEU. In real-world tasks such as poem generation, speech language generation, and music generation, SeqGAN achieves superior performance across various metrics, including human expert judgment. The generative model in SeqGAN uses recurrent neural networks (RNNs), specifically long short-term memory (LSTM) cells, to generate sequences. The discriminative model employs convolutional neural networks (CNNs) to evaluate the generated sequences. The training process involves alternating updates of the generator and discriminator, with the generator being trained using policy gradient methods and the discriminator being trained to distinguish between real and generated sequences. The results show that SeqGAN effectively addresses the limitations of traditional GANs in generating discrete sequences, achieving significant improvements in sequence generation tasks. The method's ability to balance immediate and future rewards through Monte Carlo search and its use of a discriminative model for feedback make it a promising approach for generating discrete sequences.SeqGAN is a sequence generation framework that addresses the limitations of traditional generative adversarial networks (GANs) in generating discrete token sequences. Unlike conventional GANs, which struggle with discrete outputs due to the difficulty of gradient backpropagation, SeqGAN models the data generator as a stochastic policy in reinforcement learning (RL), enabling direct gradient policy updates. The RL reward signal comes from the GAN discriminator, which evaluates the generated sequence and provides feedback through Monte Carlo search. This approach allows for effective training of the generative model by balancing immediate and future rewards. The paper proposes SeqGAN as a solution to the challenges of generating discrete sequences, where the generator is treated as an RL agent. The state is the sequence generated so far, and the action is the next token to be generated. The discriminative model evaluates the sequence and provides feedback to guide the learning of the generative model. To handle the issue of gradient backpropagation in discrete data, the generative model is treated as a stochastic parametrized policy, and Monte Carlo search is used to approximate the state-action value function. Extensive experiments on synthetic and real-world tasks demonstrate that SeqGAN significantly outperforms strong baselines, including maximum likelihood methods, scheduled sampling, and PG-BLEU. In real-world tasks such as poem generation, speech language generation, and music generation, SeqGAN achieves superior performance across various metrics, including human expert judgment. The generative model in SeqGAN uses recurrent neural networks (RNNs), specifically long short-term memory (LSTM) cells, to generate sequences. The discriminative model employs convolutional neural networks (CNNs) to evaluate the generated sequences. The training process involves alternating updates of the generator and discriminator, with the generator being trained using policy gradient methods and the discriminator being trained to distinguish between real and generated sequences. The results show that SeqGAN effectively addresses the limitations of traditional GANs in generating discrete sequences, achieving significant improvements in sequence generation tasks. The method's ability to balance immediate and future rewards through Monte Carlo search and its use of a discriminative model for feedback make it a promising approach for generating discrete sequences.