TESS 2: A Large-Scale Generalist Diffusion Language Model
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.
67b698422c8b2ef925e03f4f
Jaesung Tae
claimed_verified
2025-09-03T19:26:47.028Z
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Jake Tae
user
jaketae
67b698422c8b2ef925e03f50
Hamish Ivison
extracted_confirmed
2025-03-04T04:34:53.882Z
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Hamish Ivison
user
hamishivi
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Sachin Kumar
admin_assigned
2025-02-20T17:12:28.834Z
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sachin kumar
user
sachinkumar
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Arman Cohan
admin_assigned
2025-02-20T17:12:35.587Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1599840760465-noauth.png
Arman Cohan
user
armanc
TESS 2: A Large-Scale Generalist Diffusion Language Model
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.
TESS 2, an instruction-following diffusion language model, surpasses contemporary diffusion models and matches or exceeds strong autoregressive models through adaptation training and reward guidance at inference.
instruction-following diffusion language model diffusion loss cross-entropy instruction tuning reward guidance inference-time guidance fine-grained controllability