9 citations · 11 across the 12 of their papers we have counts for
12 papers
Deep Domain Decomposition Method for Solving the Variational Inequality Problems
Yiyang Wang, Qijia Zhou, Shengyuan Deng +1
By integrating physics-informed neural network (PINN) techniques with domain decomposition method, a deep domain decomposition method is presented for solving elliptic variational…
EtCon: Edit-then-Consolidate for Reliable Knowledge Editing
Ruilin Li, Yibin Wang, Wenhong Zhu +5
Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, achieving improv…
Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization
Chenliang Li, Adel Elmahdy, Alex Boyd +7
Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks. However, in off-policy training pipe…
ADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
Chenliang Li, Junyu Leng, Jiaxiang Li +4
Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which i…
Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach
Xinnan Zhang, Chenliang Li, Siliang Zeng +6
Aligning large language models (LLMs) with human preferences usually requires fine-tuning methods such as RLHF and DPO. These methods directly optimize the model parameters, so the…
Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment
Quan Wei, Siliang Zeng, Chenliang Li +9
Reinforcement Learning (RL) approaches have been wildly used to enhance the reasoning capabilities of Large Language Model (LLM) agents in long-horizon, multi-turn scenarios. Such…
Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality
Ruijia Zhang, Siliang Zeng, Chenliang Li +2
The goal of the Inverse reinforcement learning (IRL) task is to identify the underlying reward function and the corresponding optimal policy from a set of expert demonstrations. Wh…
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
Chenliang Li, Siliang Zeng, Zeyi Liao +4
Aligning human preference and value is an important requirement for building contemporary foundation models and embodied AI. However, popular approaches such as reinforcement learn…
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Jiaxiang Li, Siliang Zeng, Hoi-To Wai +3
Aligning human preference and value is an important requirement for contemporary foundation models. State-of-the-art techniques such as Reinforcement Learning from Human Feedback (…
On the Opportunities of Green Computing: A Survey
You Zhou, Xiujing Lin, Xiang Zhang +38
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including…
Cascadic Tensor Multigrid Method and Economic Cascadic Tensor Multigrid Method for Image Restoration Problems
Ziqi Yan, Chenliang Li, Yuhan Chen
A cascadic tensor multigrid method and an economic cascadic tensor multigrid method is presented for solving the image restoration models. The methods use quadratic interpolation a…
Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading
Zitao Song, Xuyang Jin, Chenliang Li
In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies. Neverthele…