Evaluating the alignment between textual prompts and generated images is critical for ensuring the reliability and usability of textto-image (T2I) models.However, most existing evaluation methods rely on coarsegrained metrics or static Question Answering (QA) pipelines, which lack fine-grained interpretability and struggle to reflect human preferences.To address this, we propose REVEALER, a reinforcement-guided visual reasoning framework for element-level textto-image alignment evaluation.Adopting a structured "grounding-reasoning-conclusion" paradigm, our method enables Multimodal Large Language Models (MLLMs) to explicitly localize semantic elements and derive interpretable alignment judgments.We optimize the model via Group Relative Policy Optimization (GRPO) using a multi-dimensional reward function that targets format compliance, localization precision, and alignment accuracy.Extensive experiments confirm that RE-VEALER achieves state-of-the-art results across four benchmarks.Notably, on EvalMuse-40K, it surpasses the strong proprietary Gemini 3 Pro and Training-based baselines with absolute accuracy gains of +4.2% and +13.3%, respectively.Ablation studies further demonstrate the efficacy of our method, contributing a cumulative 19.6% improvement over the base model.

HuiWei Zhou, Long Chen, Fulin Shi, Degen Huang. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.