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Datasets:
YangyiH
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openreasoning_mixed_100k

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
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Tags:
reasoning
math
code
science
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openreasoning_mixed_100k / README.md
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metadata 
pretty_name: OpenReasoning Mixed 100K
language:
  - en
task_categories:
  - text-generation
tags:
  - reasoning
  - math
  - code
  - science
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet

OpenReasoning Mixed 100K

This dataset is a 100,000-row prompt-only mixture prepared for reproducing the Qwen3-1.7B on-policy distillation experiments described in arXiv:2607.15161.

This is an independent reproduction artifact, not an official dataset release from the paper authors.

Composition

Domain Rows Upstream dataset Config / split
Math 33,334 nvidia/OpenMathReasoning default / cot
Science 33,333 nvidia/OpenScienceReasoning-2 default / train
Code 33,333 nvidia/OpenCodeReasoning split_0 / split_0

Only question text and provenance metadata are retained. Upstream answers and reasoning traces are not included.

Fields

  • messages: one-message chat record containing the user question
  • domain: math, science, or code
  • source_dataset: upstream Hugging Face dataset ID
  • source_config: upstream dataset config
  • source_split: upstream split
  • source_shard: sampled upstream Parquet shard index
  • source_id: stable source-row/problem identifier used by the builder
  • prompt_sha256: SHA-256 of the prompt text

Construction

  • Seed: 42
  • Sampling: balanced allocation over randomly ordered Parquet shards, followed by random row-group and row selection within each shard
  • Maximum selected source shards per domain: 12
  • Quality filtering: none
  • Length filtering: none
  • Prompt deduplication: none
  • Answers and reasoning traces retained: no

The exact construction manifest is included as manifest.json.

Duplicate prompts

The dataset contains 74,644 unique prompt hashes and 25,356 duplicate prompt rows. Most duplicates come from upstream reasoning datasets containing multiple solution traces for the same underlying question. They are intentionally preserved to match the row-sampling interpretation used by this reproduction.

In particular, the code portion contains 33,333 rows but 10,538 unique prompts. Users who require unique problems should deduplicate using prompt_sha256.

Upstream revisions

  • nvidia/OpenMathReasoning: d3d08664755704f422af97d43a7ff0ded4bd95df
  • nvidia/OpenScienceReasoning-2: 174b02c9cdf231f220765b2a1d5ece4550921894
  • nvidia/OpenCodeReasoning: 20a1ca19c0d050fe9057fc08339d6b370ec1c67a

Usage 

from datasets import load_dataset

dataset = load_dataset("YangyiH/openreasoning_mixed_100k", split="train")

Licensing and attribution

This repository redistributes prompt text derived from the three upstream NVIDIA datasets listed above. Review and comply with each upstream dataset card, license, terms, and source attribution requirements before use or redistribution. No single new license is asserted here over upstream content.

Limitations

  • The paper authors have not released the exact data-mixing implementation.
  • The mixture preserves duplicate prompts and should not be interpreted as 100,000 unique questions.
  • No additional quality, difficulty, contamination, or prompt-length filtering was applied.
  • The mixture has not been audited for all possible benchmark overlap or sensitive content inherited from upstream sources.