1 citations · 1 across the 4 of their papers we have counts for
4 papers
How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling Laws
Zhitao Zhu, Xili Wang, Shizhe Wu +2
High-quality data is scarce in large language model (LLM) training, yet how to schedule its use with optimization dynamics lacks theoretical guidance. We extend functional scaling…
HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench
Yueyang Wang, Jiawei Fu, Baolong Bi +2
SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabilities are fundamentally acquired durin…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
Generate to Understand for Representation
Changshang Xue, Xiande Zhong, Xiaoqing Liu
In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and…