Work

Flow-Multi: A Flow-Matching Multi-Reward Framework for Text-to-Image Generation

Year: 2026

Type: article

Abstract: Recent approaches in text-to-image (T2I) generation have actively adopted reinforcement learning (RL) techniques for human preference alignment. However, existing approaches primarily rely on a single reward function, which can lead to overfitting on specific metrics, resulting in issues such as reward hacking and imbalanced optimization among multiple objectives. To address this, we propose Flow-Multi: a flow-matching multi-reward framework for text-to-image generation. Our method builds upon flow-matching-based group-relative policy optimization (GRPO) learning. Each sample is evaluated by four reward models-based on text-to-image alignment, human preference, aesthetic quality, and GenEval-to create a multi-dimensional reward vector. We then utilize the Pareto dominance relationship to remove dominated samples and update the policy using only the non-dominated set. Additionally, we introduce advantage masking during training to suppress the contribution of low-reward samples, ensuring that only high-quality rewards are reflected in policy optimization. Experimental results demonstrate that Flow-Multi achieves balanced improvements across multiple reward criteria compared to the existing Flow-GRPO, validating the effectiveness of the multi-reward reinforcement learning framework for stable alignment in text-to-image generation.

Source: Sensors

DOI: 10.3390/s26041120

Authors: Jaegun Lee, Janghoon Choi

Institution: Kyungpook National University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Research Foundation of Korea