2 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
UP-MOPD: Update Projection in Multi-Teacher On-Policy Distillation
Taojie Zhu, Jing Jin, Yuan Xia +8
On-policy distillation from multiple teachers combines expertise from different domains in a single student, but conflicting gradients can hinder this integration. Gradient correct…
Continual Learning in Transition
Zhiyan Hou, Dan Zhang, Tao Feng +11
Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architect…
GUI-AC: Enhancing Continual Learning in GUI Agents
Can Lin, Tao Feng, Hangjie Yuan +3
Graphical User Interfaces (GUIs) serve as the dominant medium for human-computer interaction, yet building GUI agents that generalize across the vast diversity of real-world interf…
5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
Yifan Zhu, Can Lin, Hangjie Yuan +4
Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstream tasks. Although these met…
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
Yuying Li, Leqi Zheng, Yongzi Yu +6
On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on…