AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment
Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images according to prompt alignment and perceptual quality. Existing reward models are commonly trained as Bradley-Terry (BT) preference models on large-scale human preference corpora, making them costly to train, difficult to adapt, and opaque in their evaluation criteria. Meanwhile, Vision-Language Model (VLM) judges can provide more fine-grained assessments through textual rubrics, but their manually designed or heuristically generated scoring rules may fail to reliably reflect human preferences. In this paper, we propose AutoRubric-T2I, the fir...
| Authors | Kuei-Chun Kao, Daixuan Huo, Yuanhao Ban et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 20 May 2026 |
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