8 papers
UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience
Zichuan Lin, Feiyu Liu, Yijun Yang +9
Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from…
GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training
Tong Wei, Yijun Yang, Changhao Zhang +4
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent meth…
Vul-R2: A Reasoning LLM for Automated Vulnerability Repair
Xin-Cheng Wen, Zirui Lin, Yijun Yang +2
The exponential increase in software vulnerabilities has created an urgent need for automatic vulnerability repair (AVR) solutions. Recent research has formulated AVR as a sequence…
Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data
Xin-Cheng Wen, Yijun Yang, Cuiyun Gao +2
Large language models (LLMs) demonstrate considerable proficiency in numerous coding-related tasks; however, their capabilities in detecting software vulnerabilities remain limited…
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents
Siyu Zhou, Tianyi Zhou, Yijun Yang +4
Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified envir…
GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training
Tong Wei, Yijun Yang, Junliang Xing +3
Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs). Yet, its efficacy in train…
CORD: Generalizable Cooperation via Role Diversity
Kanefumi Matsuyama, Kefan Su, Jiangxing Wang +2
Cooperative multi-agent reinforcement learning (MARL) aims to develop agents that can collaborate effectively. However, most cooperative MARL methods overfit training agents, makin…
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
Siyu Zhou, Tianyi Zhou, Yijun Yang +4
Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's…