2 citations · 2 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…
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
Haoyu Wang, Xingyu Yu, Haiyan Zhao +2
Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quantization (SQ), which enables e…
UniSVQ: 2-bit Unified Scalar-Vector Quantization
Haoyu Wang, Haiyan Zhao, Xingyu Yu +4
Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration for large language models (LLMs). Scalar quantization (SQ) and vector quantizat…
Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning
Xiaoyue Xu, Sikui Zhang, Xiaorong Wang +2
Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories. Re…
Rethinking the Role of Efficient Attention in Hybrid Architectures
Ziqing Qiao, Yinuo Xu, Chaojun Xiao +6
Modern language models increasingly adopt hybrid architectures that combine full attention with efficient attention modules, such as sliding-window attention (SWA) and recurrent se…
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
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…
Densing Law of LLMs
Chaojun Xiao, Jie Cai, Weilin Zhao +7
Large Language Models (LLMs) have emerged as a milestone in artificial intelligence, and their performance can improve as the model size increases. However, this scaling brings gre…