1 citations · 2 across the 7 of their papers we have counts for
7 papers
Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs
Jing Cui, Yufei Han, Jianbin Jiao +1
Backdoor attacks embed malicious behaviors into Large Language Models (LLMs), enabling adversaries to trigger harmful outputs or bypass safety controls. However, the persistence of…
Agentic Reinforcement Learning with Implicit Step Rewards
Xiaoqian Liu, Ke Wang, Yuchuan Wu +4
Large language models (LLMs) are increasingly developed as autonomous agents using reinforcement learning (agentic RL) that reason and act in interactive environments. However, spa…
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning
Xiaoqian Liu, Ke Wang, Yongbin Li +6
Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggl…
Recent Advances in Attack and Defense Approaches of Large Language Models
Jing Cui, Yishi Xu, Zhewei Huang +3
Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their wid…
Position: Foundation Agents as the Paradigm Shift for Decision Making
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao +1
Decision making demands intricate interplay between perception, memory, and reasoning to discern optimal policies. Conventional approaches to decision making face challenges relate…
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
Xiaoqian Liu, Jianbin Jiao, Junge Zhang
Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sa…
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning
Jing Cui, Yufei Han, Yuzhe Ma +2
Backdoor attacks in reinforcement learning (RL) have previously employed intense attack strategies to ensure attack success. However, these methods suffer from high attack costs an…