Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. However, full attention incurs quadratic cost and, as resolution increases, spreads attention over increasingly redundant tokens, diluting learning signals for informativ...
Shu-Yuan Tu, Qi Tian, Yin-Ming Huang et al.· 0 citations
VA-Judger is proposed, a chain-of-thought omni-reward model that learns from pairs with clear quality gaps to establish structured output and coarse preference discrimination, then distills reliable preference explanations for harder near-quality comparisons via rejection sampling verified against human annotations, an...
Yin-Ming Huang, Shu-Yuan Tu, Xi Yan et al.· 6 citations· ⚡1
Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal Large Language Models (MLLMs) offer enhanced visual perception and reasoning, their agentic deployment faces three challenges: costly data annotation, imprecise action-in...
Jia-He Ying, Wendong Bu, Kaihang Pan et al.· 0 citations
This work states that existing visual generative models are not yet ready for RL due to the following two fundamental drawbacks that undermine the foundations of RL.
Bohan Wang, Min Zhou, Zhongqi Yue et al.· Neural Information Processin...· 0 citations
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