[2603.00918] Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00918 (cs) [Submitted on 1 Mar 2026] Title:Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards Authors:Seungwook Kim, Minsu Cho View a PDF of the paper titled Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards, by Seungwook Kim and 1 other authors View PDF HTML (experimental) Abstract:Text-to-image generation powers content creation across design, media, and data augmentation. Post-training of text-to-image generative models is a promising path to better match human preferences, factuality, and improved aesthetics. We introduce ARC (Adaptive Rewarding by self-Confidence), a post-training framework that replaces external reward supervision with an internal self-confidence signal, obtained by evaluating how accurately the model recovers injected noise under self-denoising probes. ARC converts this intrinsic signal into scalar rewards, enabling fully unsupervised optimization without additional datasets, annotators, or reward models. Empirically, by reinforcing high-confidence generations, ARC delivers consistent gains in compositional generation, text rendering and text-image alignment over the baseline. We also find that integrating ARC with external rewards results in a complementary improvement, with alleviated reward hacking. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.00918 [cs.CV]...