1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
FedBST: Bilateral Self-Training for Unsupervised Federated Classification
Aijing Yu, Kuangpu Guo, Jian Liang +1
Unsupervised federated learning with Vision-Language Models (e.g., CLIP) offers a promising solution for label-free classification, yet its performance often degrades under severe…
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation
Yongcan Yu, Jian Liang, Lingxiao He +5
While long chain-of-thought (CoT) distillation significantly improves the reasoning abilities of language models, its underlying mechanism remains poorly understood. Current pipeli…
On the Vulnerability of Parameter-Level Defenses to Model Merging
Kuangpu Guo, Qingyan Zheng, Jian Liang +4
The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized models without authorization. R…
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning
Yongcan Yu, Lingxiao He, Jian Liang +5
Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization signals from label noise. Through…