Datasets:

edev2000
/

amc12-full

A structured dataset derived from the AMC 12 (American Mathematics Competitions), designed for LLM training, evaluation, and reinforcement learning (RL) on mathematical reasoning tasks.

This repository contains all AMC 12 problems from 2000–2025, making it one of the most complete AMC12 datasets available for research.

📘 Introduction

The AMC 12 is a 25-question, 75-minute multiple-choice examination aimed at high school students. Problems are designed to increase in difficulty progressively, requiring a combination of algebra, geometry, combinatorics, and number theory reasoning.

Format: 25 multiple-choice questions (A–E)
Duration: 75 minutes
Difficulty progression: Problems 1 → 25 increase in complexity
Calculator policy:
- Since 2008, calculators are not permitted
- Problems are designed to be solvable without computational aids

Top-performing students (~top 6%) are invited to participate in the AIME, making AMC 12 a strong proxy for high-level mathematical reasoning ability.

📦 Dataset Overview

Each sample corresponds to a single AMC 12 problem.

Example (JSONL)

{
  "year": 2019,
  "problem_id": "2019A-15",
  "question": "...",
  "answer": "D",
  "difficulty": 3
}

🧱 Schema

Each entry in the dataset follows this structure:

Field	Type	Description
`problem_id`	string	Unique identifier in the format `{year}{A/B}-{problem_number}` (e.g., `2019A-15`)
`year`	int	Competition year (2000–2025)
`question`	string	Full problem statement (including choices)
`answer`	string	Correct answer option (`A`–`E`)
`difficulty`	int	Difficulty level derived from problem order

🔑 Problem ID Definition

The problem_id encodes the full provenance of each problem:

{year}{A/B}-{problem_number}

{year} → competition year
{A/B} → AMC12A or AMC12B
{problem_number} → position in the exam (1–25)

Examples

2007B-5 → AMC 12B, 2007, Problem 5
2019A-15 → AMC 12A, 2019, Problem 15

Difficulty Structure

We adopt a coarse-grained difficulty approximation aligned with problem order:

Problem Range	Difficulty
1–10	Easy–Medium (2)
11–20	Medium–Hard (3)
21–25	Hard (4)

This structure enables:

Curriculum learning
Difficulty-aware evaluation
Model capability stratification

🚀 Why This Dataset?

Compared to Other Math Datasets

Not heavily pretrained Unlike datasets such as GSM8K, AMC-style problems are less likely to be memorized by models
Higher reasoning complexity Problems typically require multi-step, structured reasoning, often exceeding datasets like MATH500
Clean evaluation signal
- Multiple-choice format eliminates ambiguity
- No unit mismatch issues (e.g., “8 months vs 240 days”)
Fully verifiable Every problem has a unique, discrete answer, ideal for RL reward design

Compared to Other AMC Datasets

Complete coverage (2000–2025) Includes all AMC12A and AMC12B problems across 25 years
Fully indexed & traceable Each problem maps directly to its original contest and position
Structured for ML pipelines Ready for:
- RL training (PPO / GRPO)
- Pass@k evaluation
- Verifier-based reward systems

📚 Data Source & Attribution

This dataset is curated from publicly available resources, with primary reference to:

Art of Problem Solving

All AMC problems are copyrighted by the Mathematical Association of America (MAA) under the American Mathematics Competitions program.

This repository does not claim ownership of the original problem statements and provides them solely for research and educational purposes.