most citedD-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models

3 citations · 4 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2025★ 1 cited

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Weixun Wang, Shaopan Xiong, Gengru Chen +38

We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user gr…

cs.CL2024★ 3 cited

D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models

Haoran Que, Jiaheng Liu, Ge Zhang +13

Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model's fundamental understanding of specific downstream domains (e.g., math and cod…