# TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation

arXiv: https://arxiv.org/abs/2011.13527 · HTML: https://mlsanity.com/paper/2011.13527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [InitialGAN: A Language GAN with Completely Random Initialization](https://mlsanity.com/paper/2208.02531/) (2022)
- [Cross-Entropy Games and Frost Training](https://mlsanity.com/paper/2605.27701/) (2026)

## Abstract (arXiv)

Score function-based natural language generation (NLG) approaches such as REINFORCE, in general, suffer from low sample efficiency and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a black box and ignore the gradient information. To improve the sample efficiency and reduce the variance of REINFORCE, we propose a novel approach, TaylorGAN, which augments the gradient estimation by off-policy update and the first-order Taylor expansion. This approach enables us to train NLG models from scratch with smaller batch size -- without maximum likelihood pre-training, and outperforms existing GAN-based methods on multiple metrics of quality and diversity. The source code and data are available at https://github.com/MiuLab/TaylorGAN