3 papers
cs.LG2025
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Zhiyuan Liang, Dongwen Tang, Yuhao Zhou +11
Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank adaptation (LoRA) reduce the cost of customizing large language models (LLMs), yet still require a separate o…
cs.LG2025
ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion
Rana Muhammad Shahroz Khan, Dongwen Tang, Pingzhi Li +2
Parameter generation has emerged as a novel paradigm for neural network development, offering an alternative to traditional neural network training by synthesizing high-quality mod…
cs.LG2025
Recurrent Diffusion for Large-Scale Parameter Generation
Kai Wang, Dongwen Tang, Wangbo Zhao +3
Parameter generation has long struggled to match the scale of today large vision and language models, curbing its broader utility. In this paper, we introduce Recurrent Diffusion f…