2 citations · 5 across the 10 of their papers we have counts for
12 papers
Beyond Corrected Actions: Training LLM Agents on Textual Critiques
Thomas Zeng, Zelei Cheng, Mingyang Zhou +3
Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions. A common a…
Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks
Zelei Cheng, Amritansh Mishra, Sambit Sahu +1
Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcement learning (RL) is a natural f…
SEAD: Competence-Aware On-Policy Distillation via Entropy-Guided Supervision
Chia-Hsuan Lee, Zelei Cheng, Yu Wang +4
On-policy distillation (OPD) has a property absent in offline distillation and RL: teacher supervision quality depends on student competence. Incoherent rollouts yield noisy gradie…
Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?
Chia-Hsuan Lee, Mingyang Zhou, Renkun Ni +6
Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of preference data drive downstream…
GPO: Learning from Critical Steps to Improve LLM Reasoning
Jiahao Yu, Zelei Cheng, Xian Wu +1
Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks. Recently, reasoning LLMs have been proposed to improve the \…
Building Coding Agents via Entropy-Enhanced Multi-Turn Preference Optimization
Jiahao Yu, Zelei Cheng, Xian Wu +1
Software engineering presents complex, multi-step challenges for Large Language Models (LLMs), requiring reasoning over large codebases and coordinated tool use. The difficulty of…
UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimality
Zelei Cheng, Xin-Qiang Cai, Yuting Tang +4
Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone for aligning large language models (LLMs) with human values. However, existing approaches struggle to cap…
A Survey on Explainable Deep Reinforcement Learning
Zelei Cheng, Jiahao Yu, Xinyu Xing
Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hin…
Soft-Label Integration for Robust Toxicity Classification
Zelei Cheng, Xian Wu, Jiahao Yu +3
Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Ther…
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
Zelei Cheng, Xian Wu, Jiahao Yu +3
Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, espe…
TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials
Zuotian Li, Xiang Liu, Zelei Cheng +3
Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified en…
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Xiaoguang Li, Hui Li, Haonan Yan +3
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through…