| --- |
| 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](https://arxiv.org/abs/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 |
|
|
| ```python |
| 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. |
|
|
