Li, Wenjun

A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

Wang, J., Jiang, H., Liu, Y., Ma, C., Zhang, X., Pan, Y., Liu, M., Gu, P., Xia, S., Chen, L., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 21

Understanding R1-Zero-Like Training: A Critical Perspective

Liu, Z., Chen, C., Li, W., Qi, P., Pang, T., Du, C., Lee, W. S., & Lin, M. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Generalization through Diversity: Improving Unsupervised Environment Design

Li, W., Varakantham, P., & Li, D. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3

Generalization through Diversity: Improving Unsupervised Environment Design

Li, W., Varakantham, P., & Li, D. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3

Improving Environment Novelty Quantification for Effective Unsupervised Environment Design

Teoh, J., Li, W., & Varakantham, P. (2024) · Advances in Neural Information Processing Systems 37

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 0

Improving Environment Novelty Quantification for Effective Unsupervised Environment Design

Teoh, J., Li, W., & Varakantham, P. (2025) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 0