title: Not All Correct Answers Are Equal: Why Your Distillation Source Matters
summary: Distillation has emerged as a practical and effective approach to enhance the
  reasoning capabilities of open-source language models. In this work, we conduct
  a large-scale empirical study on reasoning data distillation by collecting
  verified outputs from three state-of-the-art teacher models-AM-Thinking-v1,
  Qwen3-235B-A22B, and DeepSeek-R1-on a shared corpus of 1.89 million queries. We
  construct three parallel datasets and analyze their distributions, revealing
  that AM-Thinking-v1-distilled data exhibits greater token length diversity and
  lower perplexity. Student models trained on each dataset are evaluated on
  reasoning benchmarks including AIME2024, AIME2025, MATH500, and LiveCodeBench.
  The AM-based model consistently achieves the best performance (e.g., 84.3 on
  AIME2024, 72.2 on AIME2025, 98.4 on MATH500, and 65.9 on LiveCodeBench) and
  demonstrates adaptive output behavior-producing longer responses for harder
  tasks and shorter ones for simpler tasks. These findings highlight the value of
  high-quality, verified reasoning traces. We release the AM-Thinking-v1 and
  Qwen3-235B-A22B distilled datasets to support future research on open and
  high-performing reasoning-oriented language models. The datasets are publicly
  available on Hugging FaceDatasets are available on Hugging Face:
  \href{https://huggingface.co/datasets/a-m-team/AM-Thinking-v1-Distilled{AM-Thinking-v1-Distilled},
  https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled{AM-Qwen3-Distilled}.}.
paper_authors._id: 682d39e6fa24196dcd10d5e8
paper_authors.name: Xiaoyu Tian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:56.337Z
paper_authors._id: 621499d72be42a56cca7afad
paper_authors.avatarUrl: /avatars/3cea70d1a55c8096b4270093c69e4a5e.svg
paper_authors.fullname: TianXiaoyu
paper_authors.type: user
paper_authors.user: Emperorizzis
paper_authors._id: 682d39e6fa24196dcd10d5e9
paper_authors.name: Yunjie Ji
paper_authors._id: 682d39e6fa24196dcd10d5ea
paper_authors.name: Haotian Wang
paper_authors._id: 682d39e6fa24196dcd10d5eb
paper_authors.name: Shuaiting Chen
paper_authors._id: 682d39e6fa24196dcd10d5ec
paper_authors.name: Sitong Zhao
paper_authors._id: 682d39e6fa24196dcd10d5ed
paper_authors.name: Yiping Peng
paper_authors._id: 682d39e6fa24196dcd10d5ee
paper_authors.name: Han Zhao
paper_authors._id: 682d39e6fa24196dcd10d5ef
paper_authors.name: Xiangang Li
paper_title: Not All Correct Answers Are Equal: Why Your Distillation Source Matters
paper_summary: Distillation has emerged as a practical and effective approach to enhance the
  reasoning capabilities of open-source language models. In this work, we conduct
  a large-scale empirical study on reasoning data distillation by collecting
  verified outputs from three state-of-the-art teacher models-AM-Thinking-v1,
  Qwen3-235B-A22B, and DeepSeek-R1-on a shared corpus of 1.89 million queries. We
  construct three parallel datasets and analyze their distributions, revealing
  that AM-Thinking-v1-distilled data exhibits greater token length diversity and
  lower perplexity. Student models trained on each dataset are evaluated on
  reasoning benchmarks including AIME2024, AIME2025, MATH500, and LiveCodeBench.
  The AM-based model consistently achieves the best performance (e.g., 84.3 on
  AIME2024, 72.2 on AIME2025, 98.4 on MATH500, and 65.9 on LiveCodeBench) and
  demonstrates adaptive output behavior-producing longer responses for harder
  tasks and shorter ones for simpler tasks. These findings highlight the value of
  high-quality, verified reasoning traces. We release the AM-Thinking-v1 and
  Qwen3-235B-A22B distilled datasets to support future research on open and
  high-performing reasoning-oriented language models. The datasets are publicly
  available on Hugging FaceDatasets are available on Hugging Face:
  \href{https://huggingface.co/datasets/a-m-team/AM-Thinking-v1-Distilled{AM-Thinking-v1-Distilled},
  https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled{AM-Qwen3-Distilled}.}.
paper_ai_summary: Distilling reasoning data from advanced language models improves student model performance across various benchmarks.
paper_ai_keywords: distillation reasoning data teacher models AM-Thinking-v1 Qwen3-235B-A22B DeepSeek-R1 token length diversity perplexity student models reasoning benchmarks AIME2024 AIME2025 MATH500 LiveCodeBench adaptive output behavior high-quality verified reasoning traces