---
language: en
license: mit
tags:
  - math
  - reasoning
  - grpo
  - verl
  - reinforcement-learning
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/verl_deepscaler_clean_39179.parquet
---

# DeepScaleR-Verl-Clean

A single cleaned, deduplicated, verl-ready parquet built from four partial dumps of the
DeepScaleR math dataset (`agentica-org/DeepScaleR-Preview-Dataset`, MIT licensed). It is
meant for rule-based-reward GRPO/RL training with the
[verl](https://github.com/volcengine/verl) framework.

## Files

| File | Rows |
|---|---|
| `data/verl_deepscaler_clean_39179.parquet` | 39179 |
| `data/cleaning_report.json` | full paper trail of every dropped row |

## How it was built

Sources were read in priority order (`deepscaler_part1.jsonl`, `deepscaler_part2.jsonl`,
`deepscaler_export.json`, `deepscaler_legacy.jsonl`; within a file, earlier rows win):

1. **Validity** - a row is garbage unless its statement *and* its answer are both present
   and non-blank after trimming. Invalid rows are removed *before* deduplication, so a
   broken copy can never steal the slot of a good copy. Rows whose statement is unusable
   are recorded as `missing_problem`, rows with a usable statement but no answer as
   `missing_answer`. A missing `solution` is not garbage: it is stored as an empty string.
2. **Identity / deduplication** - identity is the trimmed statement text. Only the first
   occurrence (by source priority, then row order) is kept; every later copy is dropped as
   `duplicate`, including legacy copies that carry stale answers.
3. **Ordering / indexing** - survivors are sorted by statement text ascending (plain
   code-point ordering) and numbered 0..N-1; `extra_info.index` equals the row's position
   and the parquet is physically in that order. Prompts carry the trimmed statement and
   `reward_model.ground_truth` the trimmed answer.

Totals: 43608 input rows -> 39179 kept, 4390 dropped as duplicates, 39 dropped as invalid.
See `data/cleaning_report.json` for the per-row detail of everything that was thrown away.

## Schema (verl)

Five columns, in this order:

| Column | Content |
|---|---|
| `data_source` | `"DeepScaleR"` |
| `prompt` | `[{"role": "user", "content": "<trimmed problem statement>"}]` |
| `ability` | `"math"` |
| `reward_model` | `{"style": "rule", "ground_truth": "<trimmed answer>"}` |
| `extra_info` | `{"index": <row position>, "solution": "<solution, or empty string>"}` |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("dusersad12/DeepScaleR-Verl-Clean", split="train")
```