10 citations · 18 across the 12 of their papers we have counts for
13 papers
How Deep Can LLMs Learn to Reason? Expressiveness Is Key
Tianle Wang, Zhaoyang Wang, Guangchen Lan +4
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered…
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Wenzhi Fang, Liangqi Yuan, Guangchen Lan +2
Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot r…
Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
Guangchen Lan
Reinforcement learning has become a powerful paradigm for improving the capability of intelligent systems, but its practical deployment faces two central challenges. First, reinfor…
Alternating Reinforcement Learning with Contextual Rubric Rewards: Beyond the Scalarization Strategy
Guangchen Lan, Lian Xiong, Xin Zhou +7
Reinforcement Learning with Rubric Rewards (RLRR) is a framework that extends conventional reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR) by replac…
VisualActBench: Can VLMs See and Act like a Human?
Daoan Zhang, Pai Liu, Xiaofei Zhou +6
Vision-Language Models (VLMs) have achieved impressive progress in perceiving and describing visual environments. However, their ability to proactively reason and act based solely…
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang +7
As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving perfor…
Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
Guangchen Lan, Huseyin A. Inan, Sahar Abdelnabi +5
As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a…
Tensor Generalized Approximate Message Passing
Yinchuan Li, Guangchen Lan, Xiaodong Wang
We propose a tensor generalized approximate message passing (TeG-AMP) algorithm for low-rank tensor inference, which can be used to solve tensor completion and decomposition proble…
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis
Mohammadreza Kouchaki, Minglong Zhang, Aly S. Abdalla +3
In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum acc…
Bridging SFT and DPO for Diffusion Model Alignment with Self-Sampling Preference Optimization
Daoan Zhang, Guangchen Lan, Dong-Jun Han +8
Existing post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement learning (RL) methods; the former is stable during training but suffer…
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi +2
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a globa…
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies
Guangchen Lan
Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners. Current FL approaches all suffer from high communication overhead a…
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Guangchen Lan, Han Wang, James Anderson +2
Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains…