12 citations · 16 across the 15 of their papers we have counts for
4 papers · 1 filter
Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation
Ling Team, Ang Li, Ben Liu +138
We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of bi…
Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
Zuoli Tang, Junjie Ou, Kaiqin Hu +6
Recent years have witnessed significant progress in large language models' (LLMs) reasoning, which is largely due to the chain-of-thought (CoT) approaches, allowing models to gener…
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…
BOSE: A Systematic Evaluation Method Optimized for Base Models
Hongzhi Luan, Changxin Tian, Zhaoxin Huan +4
This paper poses two critical issues in evaluating base models (without post-training): (1) Unstable evaluation during training: in the early stages of pre-training, the models lac…