5 papers
Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model
Tianyi Wu, Mingzhe Du, Yue Liu +4
Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment. Existing…
MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning
Zhaolong Wang, Tongfeng Sun, Mingzheng Du +1
Vision-language pre-trained models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, and prompt learning has emerged as an efficient alternative to full fi…
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Anh Tuan, Yue Liu +6
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we int…
Efficient Reasoning via Chain of Unconscious Thought
Ruihan Gong, Yue Liu, Wenjie Qu +11
Large Reasoning Models (LRMs) achieve promising performance but compromise token efficiency due to verbose reasoning processes. Unconscious Thought Theory (UTT) posits that complex…
GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
Yue Liu, Shengfang Zhai, Mingzhe Du +9
To enhance the safety of VLMs, this paper introduces a novel reasoning-based VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the guard model to deliberativ…