Yuheng Cheng
Papers
1
Total Citations
105
H-Index
1
About
Dr. Yuheng Cheng is a leading researcher at the intersection of large language models (LLMs) and reinforcement learning (RL), with a primary focus on developing more intelligent and sample-efficient autonomous agents. His most impactful work, the 2024 survey "Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods," has already garnered over 105 citations, establishing itself as a foundational reference in this rapidly evolving field. In this comprehensive review, Dr. Cheng systematically explores how LLMs—with their vast pretrained knowledge and high-level reasoning capabilities—can address critical RL challenges, including multitask learning, sample efficiency, and complex task planning. By providing a clear taxonomy and synthesis of emerging methods, his work has helped define the research agenda for LLM-augmented RL systems. Dr. Cheng’s contributions are particularly notable for bridging the gap between language understanding and sequential decision-making, offering a roadmap for building more adaptable and capable AI agents. His research continues to influence both academic and industrial efforts to create AI systems that can learn and reason more like humans.
Research Focus
Key Achievements
Top Papers
- 1