7 papers · 1 filter
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time
Dong Yan, Jian Liang, Yanbo Wang +3
Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority vo…
Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
Zhengbo Wang, Jian Liang, Ran He +2
Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first- and second-order momenta introduces significant memory overhead, wh…
Learning Fair Domain Adaptation with Virtual Label Distribution
Yuguang Zhang, Lijun Sheng, Jian Liang +1
Unsupervised Domain Adaptation (UDA) aims to mitigate performance degradation when training and testing data are sampled from different distributions. While significant progress ha…
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
Hao Dong, Lijun Sheng, Jian Liang +3
Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks. However, their performance often remains suboptimal when direct…
The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models
Lijun Sheng, Jian Liang, Ran He +2
Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring…
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
Lijun Sheng, Jian Liang, Zilei Wang +1
Vision-language models (VLMs), such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstre…