7 papers
Data Schedules and Learning Rate Schedules Should Be Designed Together
Haodong Wen, Kairong Luo, Tingkai Yan +2
Mid-training (MT) is a training phase in LLM training pipelines that continues pretraining (PT) on smaller datasets of higher value. Taking the PT and MT phases as a whole, trainin…
SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers
Shaowen Wang, Ge Zhang, Kairong Luo +6
Looped Transformers increase effective depth by iterating a shared block of layers, but most evaluations compare at fixed model size, conflating architectural advantage with extra…
Puro-2B: Poor Lab's Qwen2-1.5B Trained on RTX 5090 within $5090
Kairong Luo, Jiarui Cui, Yaorui Yin +8
Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communities. Although strong open-so…
PCMind-2.1-Kaiyuan-2B Technical Report
Kairong Luo, Zhenbo Sun, Xinyu Shi +9
The rapid advancement of Large Language Models (LLMs) has resulted in a significant knowledge gap between the open-source community and industry, primarily because the latter relie…
How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining
Kairong Luo, Zhenbo Sun, Haodong Wen +5
Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even after sophisticated data curation. A…
Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression
Tingkai Yan, Haodong Wen, Binghui Li +3
While data scaling laws of large language models (LLMs) have been widely examined in the one-pass regime with massive corpora, their form under limited data and repeated epochs rem…
A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
Kairong Luo, Haodong Wen, Shengding Hu +5
Training large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In…