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cs.LG2026
UltraData-Code: From Raw Repositories to Algorithmically Dense and Task-Oriented Code Data
Chengying Tu, Hengyu Zhao, Shuaikang Xue +13
Code generation has become a core capability of large language models (LLMs), and code data is central to the pre-training process that develops it. As obtaining public code of suf…
cs.LG2026
Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation
Wenhui Chen, Zhifeng Li, Jie Zhou +5
A compressed student has two shapes that need not agree: the weight it deploys at inference and the weight family its training can reach. We show that a state-of-the-art weight-inh…
cs.LG2025
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
Tianyu Yu, Zefan Wang, Chongyi Wang +31
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged a…