| 
            
            | Research 
                My research interests focus on LLM agents, deep reinforcement learning, and their applications in decision-making and robotics.
               |  		
		
		  
          
            |  | Verlog: A Multi-turn RL framework for LLM agentsL Wen-Tse Chen, 
              Jiayu Chen,
              Hao Zhu,
              Jeff Schneider,
 
 
                Proposed a multi-turn reinforcement learning framework built for long-horizon LLM-agentic tasks with highly variable episode lengths.
               |  
            |  | Fine-tuning LLM Agents with Retrospective In-Context Online Learning Wen-Tse Chen, 
              Jiayu Chen, 
              Fahim Tajwar,
              Hao Zhu,
              Xintong Duan,
              Russ Salakhutdinov, 
              Jeff Schneider
 NeurIPS 2025
 NeurIPS Adaptive Foundation Models Workshop, 2024 (Oral presentation)
 
                Presented a sample-efficient method for online fine-tuning LLM agents by using in-context learning to convert sparse feedback into dense signals, enabling LLMs to adapt to dynamic environments with minimal data.
               |  
            |  | DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization Wen-Tse Chen, 
              Shiyu Huang, 
              Yuan Chiang, 
	      Tim Pearce,
	      Wei-Wei Tu,
	      Chen Ting, 
              Zhu Jun,
 AAAI 2024
 
                Proposed an on-policy framework for discovering multiple diverse optimal strategies for the same task in a single training process.
               |  |