4 papers · 1 filter
Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
Yongxian Wei, Yilin Zhao, Zixuan Hu +7
Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing a…
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
Zixuan Hu, Yongxian Wei, Li Shen +4
Model inversion, which aims to reconstruct the original training data from pre-trained discriminative models, is especially useful when the original training data is unavailable du…
UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis
Yuanrui Wang, Cong Han, Yafei Li +8
Text-to-image generation has greatly advanced content creation, yet accurately rendering visual text remains a key challenge due to blurred glyphs, semantic drift, and limited styl…
OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging
Yongxian Wei, Runxi Cheng, Weike Jin +7
Foundation models update slowly due to resource-intensive training, whereas domain-specific models evolve rapidly between releases. Model merging seeks to combine multiple expert m…