---
task_categories:
  - text-generation
pretty_name: "Skywork + DeepScaleR — Verifiable, Cross-Deduplicated"
size_categories:
  - 10K<n<100K
source_datasets:
  - Skywork/Skywork-OR1-RL-Data
  - agentica-org/DeepScaleR-Preview-Dataset
tags:
  - math
  - reinforcement-learning
  - deduplicated
  - math-verify
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
---

# Skywork + DeepScaleR — Verifiable, Cross-Deduplicated

**98,941 questions. Only parser-compatible references are included.** Here, “verifiable” means every reference component parses with Math-Verify 0.8.0, with string fallback disabled. It does not mean that the answer has been independently proved correct or that grading model outputs is error-free.

The two independently cleaned pools were merged, cross-source duplicate questions were collapsed, unresolved cross-source answer conflicts were quarantined, and only parser-compatible references were retained. The standalone datasets were not modified by merging.

## Preparation

See [PREPARATION.md](PREPARATION.md) for normalization, duplicate thresholds, conflict handling, benchmark screening, parser filtering, and limitations. [The complete preparation and publication code](https://github.com/Moe-Zbeeb/staleness-analysis/tree/95f55c5e518588771db8d761879f306ae01465c2/experiments/exact-age-14b-data) is pinned to Git commit `95f55c5e518588771db8d761879f306ae01465c2`. `manifest.json` records counts and checksums; `source-revisions.json` pins the upstream snapshots.

Only this filtered training pool is published. Raw inputs, rejected examples, benchmark questions, unfiltered pools, and models are not included.

## Loading

```python
from datasets import load_dataset

data = load_dataset("zbeeb/Skywork-DeepScaleR-Merged-Verifiable-Dedup", split="train")
```

The `train` split names the complete released pool; it is not a precomputed train/validation partition. Split by shared question groups across datasets before comparing experiments.

## Schema

- `id`: SHA-256 of the representative normalized prompt.
- `prompt`, `messages`: original representative question and its single user message.
- `answers`: ordered reference components; do not assume these are alternative accepted answers.
- `reference_answer_raw`: original source reference serialized as JSON.
- `datasets`, `source`, `source_records`: memberships and provenance; `source_records` is serialized as JSON.
- `verifiability`: parser-compatibility flags; independent label verification is false.

Separate reference solutions are not appended to prompts. Explicit answer/solution section markers trigger conservative exclusion. Native model chat templates, generated responses, rewards, and reasoning-length restrictions are not embedded in this dataset.

## Attribution and license

The upstream datasets are [Skywork/Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) and [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset), as applicable in `source-revisions.json`. DeepScaleR declares MIT; the pinned Skywork card does not declare a dataset license. This derivative does not assign a new license to upstream material. Preserve upstream attribution and consult the relevant source terms.