7 papers
Meta-Aligner: Bidirectional Preference-Policy Optimization for Multi-Objective LLMs Alignment
Wenzhe Xu, Biao Liu, Yiyang Sun +2
Multi-Objective Alignment aims to align Large Language Models (LLMs) with diverse and often conflicting human values by optimizing multiple objectives simultaneously. Existing meth…
VRM: Teaching Reward Models to Understand Authentic Human Preferences
Biao Liu, Ning Xu, Junming Yang +2
Large Language Models (LLMs) have achieved remarkable success across diverse natural language tasks, yet the reward models employed for aligning LLMs often encounter challenges of…
iScript: A Domain-Adapted Large Language Model and Benchmark for Physical Design Tcl Script Generation
Ning Xu, Zhaoyang Zhang, Senlin Shu +10
Modern EDA flows rely heavily on Tcl scripting, yet general LLMs perform poorly in this domain due to extreme data scarcity, domain-specific semantics, and the high reliability req…
Towards Understanding Feature Learning in Parameter Transfer
Hua Yuan, Xuran Meng, Qiufeng Wang +6
Parameter transfer is a central paradigm in transfer learning, enabling knowledge reuse across tasks and domains by sharing model parameters between upstream and downstream models.…
Enriching Knowledge Distillation with Intra-Class Contrastive Learning
Hua Yuan, Ning Xu, Xin Geng +1
Since the advent of knowledge distillation, much research has focused on how the soft labels generated by the teacher model can be utilized effectively. Existing studies points out…
Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
Congyu Qiao, Ning Xu, Yihao Hu +1
Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive model given training instances annotated with candidate labels related to features, among…