title: Baichuan Alignment Technical Report
summary: 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.
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paper_authors.name: Tao Zhang
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paper_authors.name: Chenzheng Zhu
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paper_authors.name: Tao Zhang
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paper_authors.name: Miao Zheng
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paper_authors.name: Xu Li
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paper_authors.name: Yijie Zhou
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paper_authors.name: Mingyang Chen
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paper_authors.name: Yanzhao Qin
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paper_authors.name: Youquan Li
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paper_authors.name: Hao Liang
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paper_authors.name: Fei Li
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paper_authors.name: Yadong Li
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paper_title: Baichuan Alignment Technical Report
paper_summary: 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.
paper_ai_summary: Baichuan Alignment provides comprehensive insights into alignment methodologies used in Baichuan models, detailing improvements through Prompt Augmentation System, Supervised Fine-Tuning, and Preference Alignment across various benchmarks.
paper_ai_keywords: Prompt Augmentation System Supervised Fine-Tuning Preference Alignment Baichuan-Instruct Qwen2-Nova-72B Llama3-PBM-Nova-70B user experience gains specialized benchmarks open-source benchmarks