The field of text-to-image generation is moving towards more advanced and nuanced methods for evaluating and improving model performance. Researchers are developing new frameworks for benchmarking fairness and utility in text-to-image models, as well as novel approaches for guiding visual metaphor generation and reinforcing chart-to-code generation. Noteworthy papers include: A Framework for Benchmarking Fairness-Utility Trade-offs in Text-to-Image Models via Pareto Frontiers, which proposes a method for evaluating fairness and utility in text-to-image models using Pareto-optimal frontiers. ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning, which develops a large-scale and highly diverse dataset and a novel chart similarity reward to improve code understanding and visual consistency. Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation, which proposes a stage-aware reinforcement learning paradigm to improve reasoning capabilities and image quality. The Mind's Eye: A Multi-Faceted Reward Framework for Guiding Visual Metaphor Generation, which introduces a self-evaluating visual metaphor generation framework that focuses on metaphor alignment. Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning, which proposes a pairwise preference reward-based GRPO method to address reward hacking and provide more stable training. OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning, which introduces a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation criteria.