Baichuan Alignment Technical Report
Abstract
We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive account of alignment methodologies, offering valuable insights for advancing AI research. We investigate the critical components that enhance model performance during the alignment process, including optimization methods, data strategies, capability enhancements, and evaluation processes. The process spans three key stages: Prompt Augmentation System(PAS), Supervised Fine-Tuning(SFT), and Preference Alignment. The problems encountered, the solutions applied, and the improvements made are thoroughly recorded. Through comparisons across well-established benchmarks, we highlight the technological advancements enabled by Baichuan Alignment. Baichuan-Instruct is an internal model, while Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Baichuan Alignment. Baichuan-Instruct demonstrates significant improvements in core capabilities, with user experience gains ranging from 17% to 28%, and performs exceptionally well on specialized benchmarks. In open-source benchmark evaluations, both Qwen2-Nova-72B and Llama3-PBM-Nova-70B consistently outperform their respective official instruct versions across nearly all datasets. This report aims to clarify the key technologies behind the alignment process, fostering a deeper understanding within the community. Llama3-PBM-Nova-70B model is available at https://huggingface.co/PKU-Baichuan-MLSystemLab/Llama3-PBM-Nova-70B.
Keywords
Cite
@article{arxiv.2410.14940,
title = {Baichuan Alignment Technical Report},
author = {Mingan Lin and Fan Yang and Yanjun Shen and Haoze Sun and Tianpeng Li and Tao Zhang and Chenzheng Zhu and Tao Zhang and Miao Zheng and Xu Li and Yijie Zhou and Mingyang Chen and Yanzhao Qin and Youquan Li and Hao Liang and Fei Li and Yadong Li and Mang Wang and Guosheng Dong and Kun Fang and Jianhua Xu and Bin Cui and Wentao Zhang and Zenan Zhou and Weipeng Chen},
journal= {arXiv preprint arXiv:2410.14940},
year = {2024}
}