Datasets:

jaival17
/

MATH

pretty_name: MATH (RLVR format)
license: mit
language:
  - en
task_categories:
  - text-generation
  - question-answering
tags:
  - math
  - reasoning
  - rlvr
  - reinforcement-learning
  - verl
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: test
        path: test.parquet

MATH (RLVR format)

This is the MATH competition-mathematics dataset (Hendrycks et al., 2021) in the verl-style RLVR (reinforcement learning with verifiable rewards) format. We used it to train and evaluate A-NSR and CW-NSR in our paper Adaptive Negative Reinforcement for LLM Reasoning: Dynamically Balancing Correction and Diversity in RLVR.

The files are an unmodified copy of the preprocessed MATH data released by Zhu et al. with The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning (NeurIPS 2025). The data_source field is TianHongZXY/MATH. We re-host the data so that our experiments can be reproduced with exactly the same files.

Dataset summary

Split	Problems	Use in our paper
`train`	7,500	RLVR training (10 epochs, prompt batch size 128)
`test`	5,000	Evaluation (Pass@k, k = 1 … 256)

Each problem has a difficulty level (1–5), one of seven subjects, a reference step-by-step solution, and a final answer (target). The final answer is used for rule-based reward checking.

Subject distribution

Subject	Train	Test
Algebra	1,744	1,187
Counting & Probability	771	474
Geometry	870	479
Intermediate Algebra	1,295	903
Number Theory	869	540
Prealgebra	1,205	871
Precalculus	746	546

Difficulty distribution

Level	Train	Test
Level 1	564	437
Level 2	1,348	894
Level 3	1,592	1,131
Level 4	1,690	1,214
Level 5	2,304	1,324
`Level ?` (unlabeled in the original data)	2	0

Data fields

Field	Type	Description
`level`	string	Difficulty, `"Level 1"` … `"Level 5"`
`type`	string	Subject (see table above)
`data_source`	string	Always `"TianHongZXY/MATH"`
`prompt`	list of `{role, content}`	Chat-format prompt: one `user` message containing the problem statement
`ability`	string	Always `"math"`
`reward_model.style`	string	Always `"rule"` (rule-based verifiable reward)
`reward_model.ground_truth.question`	string	Problem statement
`reward_model.ground_truth.solution`	string	Reference solution from MATH, with the answer in `\boxed{}`
`reward_model.ground_truth.target`	string	Final answer used for reward computation
`extra_info.index`	int	Index within the split
`extra_info.split`	string	`"train"` or `"test"`

Example

{
  "level": "Level 3",
  "type": "Algebra",
  "data_source": "TianHongZXY/MATH",
  "prompt": [{"role": "user", "content": "How many vertical asymptotes does the graph of $y=\\frac{2}{x^2+x-6}$ have?"}],
  "ability": "math",
  "reward_model": {
    "style": "rule",
    "ground_truth": {
      "question": "How many vertical asymptotes does the graph of $y=\\frac{2}{x^2+x-6}$ have?",
      "solution": "The denominator of the rational function factors into $x^2+x-6=(x-2)(x+3)$. ... Therefore, the graph has $\\boxed{2}$ vertical asymptotes.",
      "target": "2"
    }
  },
  "extra_info": {"index": 0, "split": "test"}
}

Usage

from datasets import load_dataset

ds = load_dataset("<your-username>/MATH-RLVR")
print(ds["train"][0]["prompt"][0]["content"])
print(ds["train"][0]["reward_model"]["ground_truth"]["target"])

The prompt field holds only the raw problem. In our experiments with Qwen2.5-Math-1.5B, it was wrapped in the following chat template, which is the same as in Zhu et al. and SimpleRL-Zoo:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{input}
Please reason step by step, and put your final answer
within \boxed{}.<|im_end|>
<|im_start|>assistant

The reward is binary: a response is correct if the answer in its final \boxed{} matches target under a math-equivalence check, and incorrect otherwise.

Source and license

Original dataset: MATH, by Hendrycks et al. (2021), released under the MIT License: https://github.com/hendrycks/math.
Preprocessing: Zhu et al. (2025), https://github.com/TianHongZXY/RLVR-Decomposed.
The MATH problems come from competitions such as AMC 10, AMC 12, and AIME, via the Art of Problem Solving.

Citation

If you use this dataset, please cite the original MATH paper and the work whose preprocessing we reuse:

@inproceedings{hendrycks2021math,
  title     = {Measuring Mathematical Problem Solving With the {MATH} Dataset},
  author    = {Hendrycks, Dan and Burns, Collin and Kadavath, Saurav and Arora, Akul and Basart, Steven and Tang, Eric and Song, Dawn and Steinhardt, Jacob},
  booktitle = {NeurIPS Datasets and Benchmarks},
  year      = {2021}
}

@inproceedings{zhu2025nsr,
  title     = {The Surprising Effectiveness of Negative Reinforcement in {LLM} Reasoning},
  author    = {Zhu, Xinyu and Xia, Mengzhou and Wei, Zhepei and Chen, Wei-Lin and Chen, Danqi and Meng, Yu},
  booktitle = {NeurIPS},
  year      = {2025}
}

Our paper:

@article{ingle2026adaptive,
  title  = {Adaptive Negative Reinforcement for {LLM} Reasoning: Dynamically Balancing Correction and Diversity in {RLVR}},
  author = {Ingle, Yash and Chauhan, Jaival and Yadav, Ankit and Mishra, Sudhakar},
  year   = {2026}
}

Code: A-NSR · CW-NSR