license: mit
language:
- en
task_categories:
- text-generation
tags:
- math
- reasoning
- reinforcement-learning
- deepmath
pretty_name: DeepMath RLCSD Format Difficulty Splits
DeepMath RLCSD-format difficulty splits
This dataset is derived from zwhe99/DeepMath-103K
at revision 5cf055d1fe3d7a2eb19719ac020211469736ae44 and uses the nested parquet schema released by
Leyiii/RLCSD.
Files
| Path | Rows | Difficulty |
|---|---|---|
deepmath_full/train.parquet |
103,022 | Full upstream dataset |
deepmath_easy_10k/train.parquet |
10,000 | 1.0 <= difficulty < 5.0 |
deepmath_medium_10k/train.parquet |
10,000 | 5.0 <= difficulty < 7.0 |
deepmath_hard_10k/train.parquet |
10,000 | 7.0 <= difficulty <= 10.0 |
The three subsets are pairwise disjoint by difficulty. Rows with missing required
fields and every occurrence of a duplicated normalized question are excluded from
the three sampling pools. Within each difficulty band, samples are selected
proportionally by exact half-step difficulty and then by topic.
Selection is deterministic (seed 42) and uses a SHA-256
ordering over the normalized question, source index, and pinned source revision.
Schema
Each parquet has exactly the RLCSD-style fields prompt, reward_model,
data_source, ability, and extra_info. extra_info.solution is selected uniformly from DeepMath's three R1 solutions
with Python's random.seed(42) behavior; reward_model.ground_truth comes from
final_answer.
License and attribution
The upstream DeepMath-103K dataset is released under the MIT license. Please also follow its citation and attribution requirements.