title: TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation
summary: The challenge of reducing the size of Large Language Models (LLMs) while
  maintaining their performance has gained significant attention. However,
  existing methods, such as model distillation and transfer learning, often fail
  to achieve high accuracy. To address this limitation, we introduce the
  Branch-Merge distillation approach, which enhances model compression through
  two phases: (1) the Branch Phase, where knowledge from a large teacher model is
  selectively distilled into specialized student models via
  domain-specific supervised fine-tuning (SFT); And (2) the Merge Phase, where
  these student models are merged to enable cross-domain knowledge transfer and
  improve generalization. We validate our distillation approach using DeepSeek-R1
  as the teacher and DeepSeek-R1-Distill-Qwen-32B as the student. The resulting
  merged model, TinyR1-32B-Preview, outperforms its counterpart
  DeepSeek-R1-Distill-Qwen-32B across multiple benchmarks, including Mathematics
  (+5.5 points), Coding (+4.4 points) and Science (+2.9 points), while achieving
  near-equal performance to DeepSeek-R1 on AIME 2024. The Branch-Merge
  distillation approach provides a scalable solution for creating smaller,
  high-performing LLMs with reduced computational cost and time.
paper_authors._id: 67ce5deedb623d45a95deb72
paper_authors.name: Lin Sun
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paper_authors.name: Xiaoqi Jian
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paper_authors.name: Yuhan Wu
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paper_authors.name: Weihong Lin
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paper_authors.fullname: Weihong Lin
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paper_authors.user: lwher1996
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paper_authors.name: Yongfu Zhu
paper_authors._id: 67ce5deedb623d45a95deb78
paper_authors.name: Change Jia
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paper_authors.name: Linglin Zhang
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paper_authors.fullname: Linglin zhang
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paper_authors.user: LinglinZhang
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paper_authors.name: Jinzhu Wu
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paper_authors.name: Junfeng Ran
paper_authors._id: 67ce5deedb623d45a95deb7c
paper_authors.name: Sai-er Hu
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paper_authors.fullname: Sai-er Hu
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paper_authors.name: Zihan Jiang
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paper_authors.fullname: Zihan Jiang
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paper_authors.name: Junting Zhou
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paper_authors.fullname: Antonio
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paper_authors.user: JuntingZhou
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paper_authors.name: Wenrui Liu
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paper_authors.fullname: Wenrui Liu
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paper_authors.user: CarenceLiu
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paper_authors.name: Bin Cui
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paper_authors.fullname: Bin Cui
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paper_authors.user: lazybone128
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paper_authors.name: Tong Yang
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paper_authors.name: Xiangzheng Zhang
paper_title: TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation
paper_summary: The challenge of reducing the size of Large Language Models (LLMs) while
  maintaining their performance has gained significant attention. However,
  existing methods, such as model distillation and transfer learning, often fail
  to achieve high accuracy. To address this limitation, we introduce the
  Branch-Merge distillation approach, which enhances model compression through
  two phases: (1) the Branch Phase, where knowledge from a large teacher model is
  selectively distilled into specialized student models via
  domain-specific supervised fine-tuning (SFT); And (2) the Merge Phase, where
  these student models are merged to enable cross-domain knowledge transfer and
  improve generalization. We validate our distillation approach using DeepSeek-R1
  as the teacher and DeepSeek-R1-Distill-Qwen-32B as the student. The resulting
  merged model, TinyR1-32B-Preview, outperforms its counterpart
  DeepSeek-R1-Distill-Qwen-32B across multiple benchmarks, including Mathematics
  (+5.5 points), Coding (+4.4 points) and Science (+2.9 points), while achieving
  near-equal performance to DeepSeek-R1 on AIME 2024. The Branch-Merge
  distillation approach provides a scalable solution for creating smaller,
  high-performing LLMs with reduced computational cost and time.
paper_ai_summary: The Branch-Merge distillation approach selectively transfers knowledge from a large teacher model to specialized student models and then merges them for improved generalization and reduced computational cost in LLMs.
paper_ai_keywords: Branch-Merge distillation DeepSeek-R1 DeepSeek-R1-Distill-Qwen-32B TinyR1-32B-Preview domain-specific supervised fine-tuning knowledge distillation transfer learning cross-domain knowledge transfer model compression