RLVR-GSM-MATH-IF-Mixed-Constraints
R
RLVR GSM MATH IF Mixed Constraints
Overview :
The RLVR-GSM-MATH-IF-Mixed-Constraints dataset focuses on math problems, containing various types of math questions and corresponding answers for training and validating reinforcement learning models. Its significance lies in helping develop smarter educational tools that enhance students' abilities to solve math problems. The product background information indicates that this dataset was released by Allenai on the Hugging Face platform, containing the GSM8k and MATH subsets, as well as IF Prompts with verifiable constraints, licensed under MIT License and ODC-BY license.
Target Users :
The primary audience includes educational technology developers, artificial intelligence researchers, and data scientists. This dataset is suitable for them as it provides a substantial number of math problem samples that can be used to train and test AI models in educational applications, particularly in solving math problems. Additionally, it can assist researchers in exploring how AI technology can improve student learning efficiency and academic performance.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 47.5K
Use Cases
Educational software developers use this dataset to train AI models for automatically generating answers to math problems.
Researchers analyze common mistakes students make when solving math problems using the dataset.
AI models provide personalized math learning recommendations by learning from the questions and answers within the dataset.
Features
Includes GSM8k and MATH subsets, totaling approximately 7,500 math problem samples.
The IF Prompts subset contains 14,973 samples with verifiable constraints.
Suitable for training reinforcement learning models, especially in math problem-solving.
Dataset format is compatible with open-instruct, usable for reward validation.
Covers diverse problem types, ranging from basic to more complex math problems.
Can be used to develop and test new educational technologies to enhance educational efficiency.
Applicable for research on how AI technology can improve students' math learning outcomes.
How to Use
Step 1: Visit the Hugging Face platform and locate the RLVR-GSM-MATH-IF-Mixed-Constraints dataset.
Step 2: Download the dataset, selecting the GSM8k, MATH, or IF Prompts subset as needed.
Step 3: Use the dataset to train AI models, such as reinforcement learning models, to solve math problems.
Step 4: Validate and test the model using the questions and answers in the dataset.
Step 5: Adjust parameters based on model performance to optimize accuracy and efficiency.
Step 6: Apply the trained model to actual educational software or research projects.
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase