MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and
  efficiency
Current text-to-image generative models are trained on large uncurated
datasets to enable diverse generation capabilities. However, this does not
align well with user preferences. Recently, reward models have been
specifically designed to perform post-hoc selection of generated images and
align them to a reward, typically user preference. This discarding of
informative data together with the optimizing for a single reward tend to harm
diversity, semantic fidelity and efficiency. Instead of this post-processing,
we propose to condition the model on multiple reward models during training to
let the model learn user preferences directly. We show that this not only
dramatically improves the visual quality of the generated images but it also
significantly speeds up the training. Our proposed method, called MIRO,
achieves state-of-the-art performances on the GenEval compositional benchmark
and user-preference scores (PickAScore, ImageReward, HPSv2).
69047d7c2c556835fa67f189
Nicolas Dufour
claimed_verified
2025-10-31T14:26:10.884Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
Nicolas Dufour
nicolas-dufour
user
nicolas-dufour
69047d7c2c556835fa67f18a
Lucas Degeorge
claimed_verified
2025-10-31T14:26:08.229Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63bb08b07fd5e883e13efd32/aKAR8alYsYteEQImBrWO7.jpeg
Lucas Degeorge
Lucasdegeorge
user
Lucasdegeorge
69047d7c2c556835fa67f18b
Arijit Ghosh
claimed_verified
2025-10-31T15:23:13.161Z
66f971c83d94062a4aa808ef
/avatars/f1d6c4d85d20fd4a614278ecd784c772.svg
Arijit Ghosh
arijitghosh
user
arijitghosh
69047d7c2c556835fa67f18c
Vicky Kalogeiton
69047d7c2c556835fa67f18d
David Picard
MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and
  efficiency
Current text-to-image generative models are trained on large uncurated
datasets to enable diverse generation capabilities. However, this does not
align well with user preferences. Recently, reward models have been
specifically designed to perform post-hoc selection of generated images and
align them to a reward, typically user preference. This discarding of
informative data together with the optimizing for a single reward tend to harm
diversity, semantic fidelity and efficiency. Instead of this post-processing,
we propose to condition the model on multiple reward models during training to
let the model learn user preferences directly. We show that this not only
dramatically improves the visual quality of the generated images but it also
significantly speeds up the training. Our proposed method, called MIRO,
achieves state-of-the-art performances on the GenEval compositional benchmark
and user-preference scores (PickAScore, ImageReward, HPSv2).
Conditioning text-to-image generative models on multiple reward models during training improves image quality and training efficiency.
text-to-image generative models reward models GenEval compositional benchmark PickAScore ImageReward HPSv2