Datasets:
File size: 2,637 Bytes
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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")
```
|