arXiv AI July 1, 2026 By David McAllister, Miika Aittala, Tero Karras, Janne Hellsten, Angjoo Kanazawa, Timo Aila, Samuli Laine

Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models

Read the original on arXiv AI →

arXiv:2603. 12893v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward signals to explicitly improve desirable aspects such as image quality and prompt alignment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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