4 papers
Late-to-Early Training: LET LLMs Learn Earlier, So Faster and Better
Ji Zhao, Yufei Gu, Shitong Shao +3
As Large Language Models (LLMs) achieve remarkable empirical success through scaling model and data size, pretraining has become increasingly critical yet computationally prohibiti…
SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
Qifan Yu, Xinyu Ma, Zhijian Zhuo +7
Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansio…
Robust LLM Training Infrastructure at ByteDance
Borui Wan, Gaohong Liu, Zuquan Song +32
The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanyin…
Balanced Actor Initialization: Stable RLHF Training of Distillation-Based Reasoning Models
Chen Zheng, Yiyuan Ma, Yuan Yang +11
The development of alignment and reasoning capabilities in large language models has seen remarkable progress through two paradigms: instruction tuning and reinforcement learning f…