Pedestrian head orientation recognition plays an important role in autonomous driving by providing valuable cues for understanding pedestrian attention and anticipating potential crossing behavior. However, reliable recognition in real-world traffic scenes remains challenging because pedestrian head regions are often c...
Yuan-Zhe Li, Yi-Di Huang, Xiao-Tong Chang et al.· 0 citations
Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policies can be sensitive to various policy perturbations, such as hidden-state noise, pruning, and quantization. In this work, we study how to improve pertu...
Peng-Xin Wang, Yuan-Zhe Li, Yuxin Ren et al.· 0 citations
Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-training compression, like quantization, in practical deployment, it has been observed that the unlearning effect can be substantially weakened, with the forgetting behav...
Jia-Lu Wang, Jia-Ning Deng, Shu-Qing Luo et al.· 1 citation
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