1 citations · 2 across the 15 of their papers we have counts for
6 papers · 1 filter
Hyperparameter Scaling Laws Across MoE Sparsity
Changxin Tian, Kunlong Chen, Jia Liu +3
Mixture-of-Experts (MoE) models expand model capacity without a proportional increase in training compute, but increasing sparsity makes reliable hyperparameter transfer challengin…
Effective Learning Rate Governs Loss Dynamics in Language Model Pretraining
Zihan Liu, Ruiheng Zheng, Shaobo Zhang +4
We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynamics primarily through their ratio, the effective learning rate (ELR).…
Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design
Junzhuo Li, Peijie Jiang, Changxin Tian +3
This paper presents a novel extension of neural scaling laws to Mixture-of-Experts (MoE) models, focusing on the optimal allocation of compute between expert and attention sub-laye…
MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
Jiapeng Wang, Changxin Tian, Kunlong Chen +5
Optimizing data mixtures is essential for unlocking the full potential of large language models (LLMs), yet identifying the optimal composition remains computationally prohibitive…
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…