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cs.CV2026
Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
Yuanhao Ban, I-Hung Hsu, Anastasios Angelopoulos +3
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal capture…
cs.CV2026
BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception
Zihan Chen, Hengguang Zhou, Yuan Kang +5
Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may only appear in local regions.…
cs.CV2026
Understanding Reward Hacking in Text-to-Image Reinforcement Learning
Yunqi Hong, Kuei-Chun Kao, Hengguang Zhou +1
Reinforcement learning (RL) has become a standard approach for post-training large language models and, more recently, for improving image generation models, which uses reward func…