6 papers
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Li Wang, Chen Zhang, Yirong Yang +4
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscriminately retaining both risks n…
Learning to Adapt SFT Data for Better Reasoning Generalization
Lisong Sun, Li Wang, Chen Zhang +4
Large language models (LLMs) have achieved remarkable progress, with post-training playing a crucial role in enhancing their reasoning capabilities. Among post-training paradigms,…
TAMTRL: Teacher-Aligned Reward Reshaping for Multi-Turn Reinforcement Learning in Long-Context Compression
Li Wang, Yandong Wang, Xin Yu +3
The rapid progress of large language models (LLMs) has led to remarkable performance gains across a wide range of tasks. However, when handling long documents that exceed the model…
Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition
Xuetao Lin, Tianhao Peng, Peihong Dai +2
EEG-based emotion recognition plays an important role in developing adaptive brain-computer communication systems, yet faces two fundamental challenges in practical implementations…
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
Zaitian Wang, Jian He, Yu Liang +8
Emotions play a crucial role in human behavior and decision-making, making emotion recognition a key area of interest in human-computer interaction (HCI). This study addresses the…
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Tianhao Peng, Wenjun Wu, Haitao Yuan +6
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on hetero…