11 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.CL2025
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Hardy Chen, Haoqin Tu, Fali Wang +5
This work revisits the dominant supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm for training Large Vision-Language Models (LVLMs), and reveals a key finding:…
cs.SI2024★ 1 cited
HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training
Fali Wang, Tianxiang Zhao, Junjie Xu +1
Graph self-training (GST), which selects and assigns pseudo-labels to unlabeled nodes, is popular for tackling label sparsity in graphs. However, recent study on homophily graphs s…
cs.LG2024★ 11 cited
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Fali Wang, Tianxiang Zhao, Suhang Wang
Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and un…