Wen-Tse Chen 陳文澤

I am a first year PhD student at Carnegie Mellon University (Robotics Institute) advised by Prof. Jeff Schneider. Prior to this, I pursued my undergraduate studies in Automation at Tsinghua University, where I had the privilege of working alongside Prof. Jun Zhu.

I am always open to new research collaborations. Please feel free to reach out if our interests align.

Email  /  Google Scholar  /  Github

My research interests focus on LLM agents, deep reinforcement learning, and their applications in decision-making and robotics.

Proposed a multi-turn reinforcement learning framework built for long-horizon LLM-agentic tasks with highly variable episode lengths.

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.

Enhanced IGM-based algorithms with maximum entropy RL for better exploration, ensuring locally optimal actions match global optima via an order preserving transformation, achieving SOTA performance on SAMC-v2 and Overcooked benchmark.

Proposed an on-policy framework for discovering multiple diverse optimal strategies for the same task in a single training process.

last update: Feb 2026

Copy from Dr. Jon Barron's page.