Peng, Baolin

Instruction Tuning with GPT-4

Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 183

Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., et al. (2023) · arXiv

Topic ModelingSpeech and dialogue systems

Cited by 156

Magma: A Foundation Model for Multimodal AI Agents

Yang, J., Tan, R., Wu, Q., Zheng, R., Peng, B., Liang, Y., Gu, Y., Cai, M., Ye, S., Jang, J., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 53

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Tian, Y., Peng, B., Song, L., Jin, L., Yu, D., Mi, H., & Yu, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Reinforcement World Model Learning for LLM-based Agents

Yu, X., Peng, B., Xu, R., Shen, Y., He, P., Nath, S., Singh, N., Gao, J., & Yu, Z. (2026) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 1

ThetaEvolve: Test-time Learning on Open Problems

Wang, Y., Su, S.-R., Zeng, Z., Xu, E., Ren, L., Yang, X., Huang, Z., He, X., Ma, L., Peng, B., et al. (2025) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0