TESS 2: A Large-Scale Generalist Diffusion Language Model
Authors: name: Jaesung Tae, name: Hamish Ivison, name: Sachin Kumar, name: Arman Cohan

We introduce TESS 2, a general instruction-following diffusion language model
that outperforms contemporary instruction-tuned diffusion models, as well as
matches and sometimes exceeds strong autoregressive (AR) models. We train TESS
2 by first adapting a strong AR model via continued pretraining with the usual
cross-entropy as diffusion loss, and then performing further instruction
tuning. We find that adaptation training as well as the choice of the base
model is crucial for training good instruction-following diffusion models. We
further propose reward guidance, a novel and modular inference-time guidance
procedure to align model outputs without needing to train the underlying model.
Finally, we show that TESS 2 further improves with increased inference-time
compute, highlighting the utility of diffusion LMs in having fine-grained
controllability over the amount of compute used at inference time. Code and
models are available at https://github.com/hamishivi/tess-2.