The rapid advancement of multi-modal large reasoning models (MLRMs) — enhanced versions of multi-modal large language models (MLLMs) equipped with reasoning capabilities — has revolutionized diverse applications. However, their safety implications remain underexplored. While prior work has exposed critical vulnerabilit...
Jun-Feng Fang, Yu-Kai Wang, Rui-Peng Wang et al.· AI Plus· 0 citations
Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on prompting or supervised fine-tuning, limiting their ability to adapt to unseen trajectories and changing policy contexts. We propose RePolicy, an...
Hou-Cheng Jiang, Bo-Xuan Zhang, Qi-Yong Zhong et al.· 0 citations
TrustMOPD replaces example-level teacher selection with label-free, token-level supervision allocation and closes the overall-score gap between the initial student and oracle-routed teachers when trained on single- and multi-teacher on-policy distillation.
Jie Sun, Mao Zheng, Ming-Yang Song et al.· 5 citations
Experiments on reasoning, code-generation, scientific-knowledge, scientific-knowledge, and tool-use benchmarks show that these implementations can be executed through the same verl-based backend while retaining their method-specific objectives and task-dependent performance profiles.
Jie Sun, Mao Zheng, Mingyang Song et al.· arXiv.org· 0 citations
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