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Arena Intelligence Inc
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Existing benchmarks miss the mark on faithfulness, but a new dependency-aware checklist reveals the true performance gaps in T2I models.
Rethinking supervised fine-tuning as target distribution design reveals that optimizing token likelihood may overlook richer model knowledge, leading to significant performance gains.
One-Forcing achieves state-of-the-art one-step video generation while slashing training costs to a third of previous methods.
Forget training costly reward models for text-to-image alignment – AutoRubric-T2I learns interpretable rubrics that outperform them using less than 0.01% of the data.
