8 papers
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…
MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics
Jiapeng Wang, Changxin Tian, Kunlong Chen +5
Reliable evaluation is fundamental to the progress of Large Language Models (LLMs), yet the evaluation process during pre-training is plagued by significant instability that obscur…
Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Sirui Chen, Changxin Tian, Binbin Hu +4
Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and…
WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training
Changxin Tian, Jiapeng Wang, Qian Zhao +7
Recent advances in learning rate (LR) scheduling have demonstrated the effectiveness of decay-free approaches that eliminate the traditional decay phase while maintaining competiti…
Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
Changxin Tian, Kunlong Chen, Jia Liu +3
Mixture-of-Experts (MoE) has become a dominant architecture for scaling Large Language Models (LLMs) efficiently by decoupling total parameters from computational cost. However, th…