1 citations · 1 across the 8 of their papers we have counts for
9 papers
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…
UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
Xinlong Zhao, Dongsheng Liu, Hengyu Zhao +9
As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on d…
MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling
MiniCPM Team, Wenhao An, Yingfa Chen +44
The evolution of large language models (LLMs) towards applications with ultra-long contexts faces challenges posed by the high computational and memory costs of the Transformer arc…
Data Science and Technology Towards AGI Part I: Tiered Data Management
Yudong Wang, Zixuan Fu, Hengyu Zhao +14
The development of artificial intelligence can be viewed as an evolution of data-driven learning paradigms, with successive shifts in data organization and utilization continuously…
FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
Yingli Shen, Wen Lai, Jie Zhou +7
Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a tr…
JudgeRLVR: Judge First, Generate Second for Efficient Reasoning
Jiangshan Duo, Hanyu Li, Hailin Zhang +3
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for reasoning in Large Language Models. However, optimizing solely for final-answer correctness…
NOSA: Native and Offloadable Sparse Attention
Yuxiang Huang, Pengjie Wang, Jicheng Han +9
Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-free KV cache offloa…
Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data
Yudong Wang, Zixuan Fu, Jie Cai +9
Data quality has become a key factor in enhancing model performance with the rapid development of large language models (LLMs). Model-driven data filtering has increasingly become…
PyBench: Evaluating LLM Agent on various real-world coding tasks
Yaolun Zhang, Yinxu Pan, Yudong Wang +1
The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image editing. However, existing benchmarks…