arXiv AI
Oct 5

Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards

The paper presents a post‑training approach for text‑to‑image models that combines a preference reward, trained on large human preference data, with rubric‑based rewards that assess prompt faithfulness and other desirable traits. The authors show that a simple reward composition strategy outperforms a naive weighted average, leading to significant Elo gains on the Arena leaderboard for models like Flux2dev and Ideogram‑4. They also release Arena‑T2I‑Training, a 1K subset of data to aid reproducible research in post‑training.

By Yuanhao Ban, I-Hung Hsu, Anastasios Angelopoulos, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh
arXiv Computer Vision
18h ago

AdvFidelity: Unifying Preference Alignment and Few-Step Image Generation within Reinforcement Learning

arXiv:2610.29380v1 Announce Type: new Abstract: Practical image generation requires both human preference alignment and few-step efficiency, yet combining reinforcement learning (RL) with distillatio...

By Sicong Kang, Fangao Zeng, Yuhan Li, Hao Zhou, Mengfei Xu, Wenxiang Shang, Wei Li, Bingbing Ni, Pipei Huang