Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Authors: name: Xiaoyu Tian, name: Yunjie Ji, name: Haotian Wang, name: Shuaiting Chen, name: Sitong Zhao, name: Yiping Peng, name: Han Zhao, name: Xiangang Li

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}.}.