README.md

metadata

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 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

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