4 citations · 5 across the 10 of their papers we have counts for
12 papers
Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards
Keru Chen, Sen Lin, Yingbin Liang +2
Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing information, and incorporating teammat…
Theory on Attention Dynamics for Out-of-Distribution In-Context Learning
Junze Deng, Daouda Sow, Sen Lin +1
Transformers have demonstrated remarkable in-context learning (ICL) capabilities, enabling them to perform new tasks without additional fine-tuning. However, their performance ofte…
Constraint-Rectified Training for Efficient Chain-of-Thought
Qinhang Wu, Sen Lin, Ming Zhang +2
Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) based post-t…
Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation
Pei-Chi Pan, Yingbin Liang, Sen Lin
Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning (RL)-based fine-tuning is a key m…
Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems
Hongbo Li, Qinhang Wu, Sen Lin +2
Mixture-of-Experts (MoE) models improve transformer efficiency but lack a unified theoretical explanation, especially when both feed-forward and attention layers are allowed to spe…
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective
Junze Deng, Qinhang Wu, Peizhong Ju +3
Rehearsal-based methods have shown superior performance in addressing catastrophic forgetting in continual learning (CL) by storing and training on a subset of past data alongside…
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
Siyuan Mu, Sen Lin
Artificial intelligence (AI) has achieved astonishing successes in many domains, especially with the recent breakthroughs in the development of foundational large models. These lar…
Towards Fast Safe Online Reinforcement Learning via Policy Finetuning
Keru Chen, Honghao Wei, Zhigang Deng +1
The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods. While offline s…
Theory on Mixture-of-Experts in Continual Learning
Hongbo Li, Sen Lin, Lingjie Duan +2
Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been ident…
Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks
Yung-Fu Chen, Sen Lin, Anish Arora
We propose a learning algorithm for local routing policies that needs only a few data samples obtained from a single graph while generalizing to all random graphs in a standard mod…
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
Sen Lin, Daouda Sow, Kaiyi Ji +2
Bilevel optimization has become a powerful tool in a wide variety of machine learning problems. However, the current nonconvex bilevel optimization considers an offline dataset and…
Doubly Robust Instance-Reweighted Adversarial Training
Daouda Sow, Sen Lin, Zhangyang Wang +1
Assigning importance weights to adversarial data has achieved great success in training adversarially robust networks under limited model capacity. However, existing instance-rewei…