1 citations · 1 across the 1 of their papers we have counts for
5 papers
SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
Fangxun Shu, Yongjie Ye, Yue Liao +6
We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them wh…
SAIL-VL2 Technical Report
Weijie Yin, Yongjie Ye, Fangxun Shu +11
We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves…
SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement
Weijie Yin, Dingkang Yang, Hongyuan Dong +5
Vision Transformers (ViTs) are essential as foundation backbones in establishing the visual comprehension capabilities of Multimodal Large Language Models (MLLMs). Although most Vi…
Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
Haochen Wang, Xiangtai Li, Zilong Huang +9
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human "thinking with images". However, no benchmark exists to evaluate…
VGR: Visual Grounded Reasoning
Jiacong Wang, Zijian Kang, Haochen Wang +8
In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias…