1 citations · 1 across the 8 of their papers we have counts for
10 papers
Esports World Model Dataset
Weiyu Ma, Zeyuan Gong, Xuhui Liu +5
Interactive world models must capture how multiple participants jointly change a shared environment while observing different parts of it. Competitive games make this challenge con…
Hybrid Advantage Estimation with Unified Critic for VLM Agentic Reinforcement Learning
Wenxuan Zhang, Yuhui Wang, Donggang Jia +5
Large Vision-Language Models (VLMs) now act as agents in interactive environments, where success requires coherent reasoning and decision-making across turns. Although end-to-end t…
Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs
Meng Lu, Ran Xu, Yi Fang +14
While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to "think with images", i.e., to reason through multi-step visual interactions, rem…
Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding
Xiaoqian Shen, Wenxuan Zhang, Jun Chen +1
Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond c…
Query-based Knowledge Transfer for Heterogeneous Learning Environments
Norah Alballa, Wenxuan Zhang, Ziquan Liu +3
Decentralized collaborative learning under data heterogeneity and privacy constraints has rapidly advanced. However, existing solutions like federated learning, ensembles, and tran…
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
Wenxuan Zhang, Philip H. S. Torr, Mohamed Elhoseiny +1
Fine-tuning large language models (LLMs) on human preferences, typically through reinforcement learning from human feedback (RLHF), has proven successful in enhancing their capabil…
Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation
Wenxuan Zhang, Youssef Mohamed, Bernard Ghanem +3
We propose and study a realistic Continual Learning (CL) setting where learning algorithms are granted a restricted computational budget per time step while training. We apply this…
Label Delay in Online Continual Learning
Botos Csaba, Wenxuan Zhang, Matthias Müller +4
Online continual learning, the process of training models on streaming data, has gained increasing attention in recent years. However, a critical aspect often overlooked is the lab…
ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction
Wenxuan Zhang, Xuechao Zou, Li Wu +3
Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It is essential in numerous domains, such as traffic flow prediction and…
Overcoming Generic Knowledge Loss with Selective Parameter Update
Wenxuan Zhang, Paul Janson, Rahaf Aljundi +1
Foundation models encompass an extensive knowledge base and offer remarkable transferability. However, this knowledge becomes outdated or insufficient over time. The challenge lies…