5 papers
Understanding Trajectory Heterogeneity in Federated World Model Learning
Yipan Wei, Zhaokun Yan, Ziming Hong +2
World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while owners…
Positive-Unlabeled Learning for Agent Safety False Alarm Auditing
Xichen Yan, Chongyang Gao, Kezhen Chen +3
Safety monitors help safeguard language-model agents interacting with external tools and environments, but conservative monitoring can generate many false alarms, consuming extensi…
GAP: Graph-Based Agent Planning with Parallel Tool Use and Reinforcement Learning
Jiaqi Wu, Qinlao Zhao, Zefeng Chen +4
Autonomous agents powered by large language models (LLMs) have shown impressive capabilities in tool manipulation for complex task-solving. However, existing paradigms such as ReAc…
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Kai Qin, Jiaqi Wu, Jianxiang He +8
As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM un…
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
Haoyuan Sun, Jiaqi Wu, Bo Xia +7
Standing in 2025, at a critical juncture in the pursuit of Artificial General Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated significant potential in enhancin…