1 citations · 1 across the 3 of their papers we have counts for
4 papers
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,…
What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
Yuze Zhao, Junpeng Fang, Lu Yu +6
Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the claim that code improves rea…
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ling Team, Binwei Zeng, Chao Huang +71
In this technical report, we tackle the challenges of training large-scale Mixture of Experts (MoE) models, focusing on overcoming cost inefficiency and resource limitations preval…
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
Zhixuan Chu, Yan Wang, Feng Zhu +3
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language flue…