Transfer Learning for Text Diffusion Models
Kehang Han Kathleen Kenealy Aditya Barua Noah Fiedel Noah Constant
In this report, we explore the potential for text diffusion to replace
autoregressive (AR) decoding for the training and deployment of large language
models (LLMs). We are particularly interested to see whether pretrained AR
models can be transformed into text diffusion models through a lightweight
adaptation procedure we call ``AR2Diff''. We begin by establishing a strong
baseline setup for training text diffusion models. Comparing across multiple
architectures and pretraining objectives, we find that training a decoder-only
model with a prefix LM objective is best or near-best across several tasks.
Building on this finding, we test various transfer learning setups for text
diffusion models. On machine translation, we find that text diffusion
underperforms the standard AR approach. However, on code synthesis and
extractive QA, we find diffusion models trained from scratch outperform AR
models in many cases. We also observe quality gains from AR2Diff -- adapting AR
models to use diffusion decoding. These results are promising given that text
diffusion is relatively underexplored and can be significantly faster than AR
decoding for long text generation.