1 citations · 1 across the 3 of their papers we have counts for
8 papers
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
Wendong Bu, Yang Wu, Qifan Yu +10
As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations,…
FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL
Kaihang Pan, Wendong Bu, Yuruo Wu +7
Recent studies extend the autoregression paradigm to text-to-image generation, achieving performance comparable to diffusion models. However, our new PairComp benchmark -- featurin…
Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning
Kaihang Pan, Yang Wu, Wendong Bu +9
Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if the…
Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program
Minghe Gao, Xuqi Liu, Zhongqi Yue +7
Recent advancements in reward signal usage for Large Language Models (LLMs) are remarkable. However, significant challenges exist when transitioning reward signal to the multimodal…
Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark
Bingchen Miao, Yang Wu, Minghe Gao +7
The development of Generalist Virtual Agents (GVAs) has shown significant promise in autonomous task execution. However, current training paradigms face critical limitations, inclu…
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Qifan Yu, Zhebei Shen, Zhongqi Yue +7
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduc…