| --- |
| pretty_name: AMC 2023 (RLVR format) |
| license: other |
| license_name: maa-competition-problems |
| license_link: https://maa.org/ |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - math |
| - reasoning |
| - rlvr |
| - competition-math |
| - amc |
| - evaluation |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: test.parquet |
| --- |
| |
| # AMC 2023 (RLVR format) |
|
|
| This dataset contains **40 problems from the 2023 AMC 12 (AMC 12A and AMC 12B)**, each with its final answer, in the verl-style RLVR (reinforcement learning with verifiable rewards) format. In our paper *Adaptive Negative Reinforcement for LLM Reasoning: Dynamically Balancing Correction and Diversity in RLVR*, it is one of our three evaluation benchmarks, of moderate difficulty between MATH and AIME 2025. |
|
|
| The file is an unmodified copy of the AMC23 evaluation data released by Zhu et al. with *The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning* (NeurIPS 2025). The `data_source` field is `TianHongZXY/amc23`. We re-host the data so that our experiments can be reproduced with exactly the same file. |
|
|
| > **Evaluation only.** This dataset is intended for evaluation. Do not train on it. |
|
|
| ## Dataset summary |
|
|
| | Split | Problems | Use in our paper | |
| |-------|---------:|------------------| |
| | `test` | 40 | Evaluation (Pass@k, k = 1 … 256, 256 samples per problem) | |
|
|
| | Source contest | Problems | |
| |----------------|---------:| |
| | 2023 AMC 12A | 22 | |
| | 2023 AMC 12B | 18 | |
|
|
| The original AMC contests are multiple choice. Here each problem is posed **without the answer choices**, and the model must produce the numerical answer itself. |
|
|
| Because there are only 40 problems, one problem corresponds to 2.5 percentage points of Pass@k. |
|
|
| ## Data fields |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | int | Problem id (0–39) | |
| | `url` | string | Link to the problem on the Art of Problem Solving wiki | |
| | `data_source` | string | Always `"TianHongZXY/amc23"` | |
| | `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.target` | string | Final answer | |
| | `extra_info.index` | int | Index within the split | |
| | `extra_info.split` | string | Always `"test"` | |
|
|
| No reference solutions are included. Solutions can be found through the `url` field. |
|
|
| ### Example |
|
|
| ```json |
| { |
| "id": 0, |
| "url": "https://artofproblemsolving.com/wiki/index.php/2023_AMC_12A_Problems/Problem_1", |
| "data_source": "TianHongZXY/amc23", |
| "prompt": [{"role": "user", "content": "Cities $A$ and $B$ are $45$ miles apart. Alicia lives in $A$ and Beth lives in $B$. Alicia bikes towards $B$ at 18 miles per hour. Leaving at the same time, Beth bikes toward $A$ at 12 miles per hour. How many miles from City $A$ will they be when they meet?"}], |
| "ability": "math", |
| "reward_model": { |
| "style": "rule", |
| "ground_truth": { |
| "question": "Cities $A$ and $B$ are $45$ miles apart. ...", |
| "target": "27" |
| } |
| }, |
| "extra_info": {"index": 0, "split": "test"} |
| } |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("<your-username>/AMC23-RLVR", split="test") |
| for row in ds: |
| question = row["prompt"][0]["content"] |
| answer = row["reward_model"]["ground_truth"]["target"] |
| ``` |
|
|
| In our experiments with `Qwen2.5-Math-1.5B`, the problem was wrapped in this chat template: |
|
|
| ``` |
| <|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 |
| ``` |
|
|
| Generation settings: temperature 0.6, top-p 0.95, 256 samples per problem. Pass@k was computed with the unbiased estimator of Chen et al. (2021). |
|
|
| ## Source and license |
|
|
| - **Problems:** 2023 AMC 12A and AMC 12B, © Mathematical Association of America (MAA). The problems are reproduced here for non-commercial research and evaluation. Copyright remains with the MAA. |
| - **Problem text and links:** Art of Problem Solving wiki. |
| - **Preprocessing:** Zhu et al. (2025), <https://github.com/TianHongZXY/RLVR-Decomposed>. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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: |
|
|
| <!-- Replace with the final citation once the paper is published. --> |
| ```bibtex |
| @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](https://github.com/Jaival111/Adaptive-Negative-Reinforcement) · [CW-NSR](https://github.com/Jaival111/Confidence-Weighted-NSR) |
|
|
