license: cc-by-4.0
task_categories:

• text-generation
  language:
• en
  tags:
• code
• math
• scientific-qa
• instruction-following
• reasoning
• thinking
• gpt-oss-120b
• distill
  size_categories:
• 435K
  configs:
• config_name: stage1
  data_files:
  • split: math
    path: stage1/math/*parquet
  • split: code
    path: stage1/code/*parquet
  • split: science
    path: stage1/science/*parquet
  • split: instruction_following
    path: stage1/instruction_following/*parquet
• config_name: stage2
  data_files:
  • split: math
    path: stage2/math/*parquet
  • split: code
    path: stage2/code/*parquet
  • split: science
    path: stage2/science/*parquet
  • split: instruction_following
    path: stage2/instruction_following/*parquet

Superior-Reasoning-SFT-gpt-oss-120b-Logprob

🚀 Overview

This dataset contains the token-level log-probabilities generated by the teacher model (gpt-oss-120b) for the reasoning samples in the main Superior-Reasoning-SFT-gpt-oss-120b Dataset.

🔗 Relationship to Main Dataset

This dataset is a companion to the main Superior-Reasoning-SFT-gpt-oss-120b dataset. Records are linked via a unique sample_uuid.

• Main Dataset: Contains the text (prompts, responses), domain info, and high-level metadata.
• Logprobs Dataset (This): Contains the token IDs and their corresponding log-probability values from the teacher.

📄 Data Format

The data in the DASD-Thinking dataset follows a structured format:

Example:

{
    "sample_uuid": "e6ebfe0b-62a2-4b8b-ac53-4c8d48facbea",
    "token_ids": [2167, 1309, ...],
    "logprobs": [-0.08339496701955795, -1.2306615114212036, ...],
}

📊 Proven Effectiveness

Models trained on this specific dataset recipe achieve State-of-the-Art performance for their size class.

4B Dense Model Performance

30B MoE Model Performance

DASD-30B-A3B-Thinking-Preview (trained on Stage 1 data only) demonstrates incredible data efficiency.

📜 Dataset Access & License

The dataset is released under CC BY 4.0.

📚 Citation

DASD-Thinking is developed by Alibaba Cloud, as part of our mission to advance open, efficient, and trustworthy reasoning systems. If you find this work useful in your research or applications, please cite our technical report.

@article{yan2026dasd,
  title={Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning},
  author={Yan, Shaotian and Liu, Kaiyuan and Shen, Chen and Wang, Bing and Fan, Sinan and Zhang, Jun and Wu, Yue and Wang, Zheng and Ye, Jieping},
  year={2026},
  journal={arXiv preprint arXiv:2601.09088},
  url={https://arxiv.org/abs/2601.09088}
}
    
@article{liu2025where,
  title={Where Did This Sentence Come From? Tracing Provenance in LLM Reasoning Distillation},
  author={Liu, Kaiyuan and Yan, Shaotian and Miao, Rui and Wang, Bing and Shen, Chen and Zhang, Jun and Ye, Jieping},
  journal={arXiv preprint arXiv:2512.20908},
  year={2025}
}

We welcome collaboration, feedback, and community contributions to push the boundaries of what small models can reason about—transparently and responsibly.