mlsanityData to 7 Oct 2026

TextGAIL: Generative Adversarial Imitation Learning for Text Generation

arXiv 2004.13796 ↗First posted 7 Apr 2020cs.CL cs.LGcited by 28

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Abstract (arXiv)

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. Our approach uses contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 9; 2024: 4; 2025: 4; 2026: 1.

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  1. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244
  2. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  3. 2023RADAR: Robust AI-Text Detection via Adversarial Learningcited by 187
  4. 2022StyleGAN-Human: A Data-Centric Odyssey of Human Generationcited by 143
  5. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  6. 2023Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Gamecited by 27
  7. 2024Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learningcited by 27
  8. 2023Generating Summaries with Controllable Readability Levelscited by 24
  9. 2024Imitating Language via Scalable Inverse Reinforcement Learningcited by 21
  10. 2022A survey on text generation using generative adversarial networkscited by 16
  11. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  12. 2022KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learningcited by 9
16 more
  1. 2025SGIC: A Self-Guided Iterative Calibration Framework for RAGcited by 8
  2. 2021How Deep Are the Fakes? Focusing on Audio Deepfake: A Surveycited by 6
  3. 2021Summary Grounded Conversation Generationcited by 5
  4. 2021Adaptive Bridge between Training and Inference for Dialoguecited by 5
  5. 2025From Demonstrations to Rewards: Alignment Without Explicit Human Preferencescited by 4
  6. 2021A Single Example Can Improve Zero-Shot Data Generationcited by 2
  7. 2023Adversarial learning of neural user simulators for dialogue policy optimisationcited by 2
  8. 2024An Empirical Study on Self-correcting Large Language Models for Data Science Code Generationcited by 2
  9. 2025Language Model Distillation: A Temporal Difference Imitation Learning Perspectivecited by 2
  10. 2021Generative Adversarial Imitation Learning for Empathy-based AIcited by 0
  11. 2021OptAGAN: Entropy-based finetuning on text VAE-GANcited by 0
  12. 2023Enhancing Text Generation with Cooperative Trainingcited by 0
  13. 2023On Combining Expert Demonstrations in Imitation Learning via Optimal Transportcited by 0
  14. 2024Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminatorcited by 0
  15. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advancescited by 0
  16. 2026Latent Adversarial Regularization for Offline Preference Optimizationcited by 0

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  1. 2018Toward Diverse Text Generation with Inverse Reinforcement Learning
  2. 2018Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
  3. 2023Improved Training of Mixture-of-Experts Language GANs
  4. 2020Text Generation by Learning from Demonstrations
  5. 2020Informed Sampling for Diversity in Concept-to-Text NLG
  6. 2023StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
  7. 2024Dynamic Prompt Optimizing for Text-to-Image Generation
  8. 2023WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models