AutoRubric-T2I learns and selects explicit rubrics from preference pairs to guide VLM judges, producing high-quality interpretable rewards for T2I alignment with far less data than traditional Bradley-Terry models.
Rubricrl: Simple generalizable rewards for text-to-image generation
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C2 synthesizes contrastive helpful/misleading rubric pairs from binary preferences to train cooperative generators and critical verifiers, yielding up to 6.5-point gains on RM-Bench and enabling smaller models to match larger rubric-augmented ones.
Lens is a 3.8B-parameter text-to-image model that reaches competitive or superior performance to >6B-parameter systems using 19.3% of the training compute of Z-Image through a densely captioned 800M dataset, multi-resolution batching, semantic VAE, strong language encoder, RL fine-tuning, and 4-step
citing papers explorer
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AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment
AutoRubric-T2I learns and selects explicit rubrics from preference pairs to guide VLM judges, producing high-quality interpretable rewards for T2I alignment with far less data than traditional Bradley-Terry models.
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C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
C2 synthesizes contrastive helpful/misleading rubric pairs from binary preferences to train cooperative generators and critical verifiers, yielding up to 6.5-point gains on RM-Bench and enabling smaller models to match larger rubric-augmented ones.
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Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models
Lens is a 3.8B-parameter text-to-image model that reaches competitive or superior performance to >6B-parameter systems using 19.3% of the training compute of Z-Image through a densely captioned 800M dataset, multi-resolution batching, semantic VAE, strong language encoder, RL fine-tuning, and 4-step