Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis
Zicheng Kong · Dehua Ma · Zhenbo Xu · Anwen Yang · Yiwei Ru · Haoran Wang · Zixuan Zhou · Fuqing Bie · Liuyu Xiang · Huijia Wu · Jian Zhao · Zhaofeng He
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Zicheng Kong · Dehua Ma · Zhenbo Xu · Anwen Yang · Yiwei Ru · Haoran Wang · Zixuan Zhou · Fuqing Bie · Liuyu Xiang · Huijia Wu · Jian Zhao · Zhaofeng He
Dianyun Wang · Qingsen Ma · Yuhu Shang · Zhifeng Lu · Zhenbo Xu · Lechen Ning · Huijia Wu · Zhaofeng He
Safety alignment—training large language models (LLMs) to refuse harmful requests while remaining helpful—is critical for responsible deployment. Prior work established that safety behaviors are governed by low-rank structures, suggesting parameter-efficient fine-tuning (PEFT) should be well-suit...
Zixuan Zhou · Yujun Diao · Zicheng Kong · Dehua Ma · Zhenbo Xu · Pei Pei Li · Zhaofeng He
Large Language Models (LLMs) are primarily constrained by memory and bandwidth bottlenecks during deployment. Although Vector Quantization (VQ) has emerged as a promising solution, existing methods incur inference overhead due to massive codebook storage and intensive index lookups. Moreover, the...
Yu Li · Xiaojie Zhang · Ruihai Wu · Zilong Zhang · Yiran Geng · Hao Dong · Zhaofeng He
Yuhu Shang · Xiang Cheng · Yimeng Ren · Huijia Wu · Xuexiong Luo · Kangkang Lu · Jian Zhao · Zhaofeng He