Baichuan Alignment Technical Report
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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Baichuan Alignment Technical Report
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.
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.
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