5 citations · 13 across the 14 of their papers we have counts for
8 papers · 1 filter
SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning
Yi Li, Linan Yue, Weibo Gao
Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from multiple sampled responses. Ho…
DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation
Ao Yu, Weibo Gao, Heng Zhou +6
On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answ…
Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call
Weibo Gao, Qi Liu, Linan Yue +6
Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement. Learner simul…
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
Rui Lv, Zaixi Zhang, Kai Zhang +6
Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in t…
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Weibo Gao, Qi Liu, Linan Yue +4
Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learner…
Towards Few-shot Self-explaining Graph Neural Networks
Jingyu Peng, Qi Liu, Linan Yue +3
Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as…
Cooperative Classification and Rationalization for Graph Generalization
Linan Yue, Qi Liu, Ye Liu +3
Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) d…
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
Linan Yue, Qi Liu, Yichao Du +3
The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to suppo…