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arXiv · 2603.00918

Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards

Seungwook Kim·Minsu Cho
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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 improve human preference alignment, factuality, and aesthetics. We introduce SOLACE (Self-Originating LAtent Confidence Estimation), a post-training framework that replaces external reward supervision with an internal self-confidence signal: we re-noise the model's own outputs and measure how accurately it recovers the injected noise, treating low reconstruction error as high self-confidence. SOLACE converts this intrinsic signal into scalar rewards for reinforcement learning, requiring no external reward models, annotators, or preference data. By reinforcing high-confidence generations, SOLACE delivers consistent gains in compositional generation, text rendering, and text-image alignment. Integrating SOLACE with external rewards yields complementary improvements while alleviating reward hacking.

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Seungwook Kim, Minsu Cho. 2026-03-01. Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards. https://arxiv.org/abs/2603.00918

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