# Transfer Learning for Text Diffusion Models

**Authors:** Kehang Han, Kathleen Kenealy, Aditya Barua, Noah Fiedel, Noah Constant  
**arXiv:** [2401.17181](https://arxiv.org/abs/2401.17181)  
**Date:** 2024-01-30  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2401.17181v1/

## Key points

- Text diffusion models are explored as an alternative to autoregressive (AR) decoding in training and deploying large language models (LLMs)
- Training a decoder-only model with a prefix LM objective yields the best or near-best results across multiple tasks
- Text diffusion models outperform AR models in code synthesis and extractive QA when trained from scratch
- Adapting AR models to use diffusion decoding through the AR2Diff procedure results in quality gains
- Text diffusion offers significant advantages in terms of speed compared to AR decoding for generating long texts
- Further research is needed to explore the potential of text diffusion and identify effective setups

## Summary

In their report titled "Transfer Learning for Text Diffusion Models," authors Kehang Han, Kathleen Kenealy, Aditya Barua, Noah Fiedel, and Noah Constant explore the potential of using text diffusion as an alternative to autoregressive (AR) decoding in training and deploying large language models (LLMs). They establish a strong baseline setup for training text diffusion models by comparing different architectures and pretraining objectives. Their findings show that training a decoder-only model with a prefix LM objective yields the best or near-best results across multiple tasks. The authors then test various transfer learning setups for text diffusion models and find that while it may underperform in machine translation compared to the standard AR approach, it outperforms AR models in code synthesis and extractive QA when trained from scratch. Furthermore, they observe quality gains from adapting AR models to use diffusion decoding through the AR2Diff procedure. These results are promising as text diffusion offers significant advantages in terms of speed compared to AR decoding for generating long texts. Overall, this report provides valuable insights into the potential of text diffusion as a replacement for autoregressive decoding in training and deploying large language models. It highlights effective setups while also identifying areas where further research is needed. is explored as an alternative to  in  of . The authors' experiments demonstrate the effectiveness of certain setups while also highlighting areas where further research is needed.