2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
A Faster Path to Continual Learning
Wei Li, Hangjie Yuan, Zixiang Zhao +3
Continual Learning (CL) aims to train neural networks on a dynamic stream of tasks without forgetting previously learned knowledge. Among optimization-based approaches, C-Flat has…
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
Aojun Lu, Tao Feng, Hangjie Yuan +2
The adaptation of large-scale Vision-Language Models (VLMs) through post-training reveals a pronounced generalization gap: models fine-tuned with Reinforcement Learning (RL) consis…
Continual GUI Agents
Ziwei Liu, Borui Kang, Hangjie Yuan +4
As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained on static environments deteriora…