7 papers
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
Yuxiao Ye, Yiwen Zhang, Huiyuan Xie +2
LLM-based multi-agent systems are increasingly used for strategic decision-making tasks. In such settings, performance depends not only on individual model capabilities, but also o…
Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization
Xin Yu, Liuchen Liao, Yiwen Zhang +3
On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a stronger external teacher has…
DemoTuner: Automatic Performance Tuning for Database Management Systems Based on Demonstration Reinforcement Learning
Hui Dou, Lei Jin, Yuxuan Zhou +3
The performance of modern DBMSs such as MySQL and PostgreSQL heavily depends on the configuration of performance-critical knobs. Manual tuning these knobs is laborious and ineffici…
Jailbreaking LLMs via Semantically Relevant Nested Scenarios with Targeted Toxic Knowledge
Ning Xu, Bo Gao, Hui Dou
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks. However, they remain exposed to jailbreak attacks, eliciting harmful responses. The nested…
Adjusting the Output of Decision Transformer with Action Gradient
Rui Lin, Yiwen Zhang, Zhicheng Peng +1
Decision Transformer (DT), which integrates reinforcement learning (RL) with the transformer model, introduces a novel approach to offline RL. Unlike classical algorithms that take…
ToMPO: Training LLM Strategic Decision Making from a Multi-Agent Perspective
Yiwen Zhang, Ziang Chen, Fanqi Kong +2
Large Language Models (LLMs) have been used to make decisions in complex scenarios, where they need models to think deeply, reason logically, and decide wisely. Many existing studi…