English
RLVR-GSM-MATH-IF-Mixed-Constraints dataset is a dataset focusing on mathematical problems. It contains multiple types of mathematical problems and corresponding solutions for training and validating reinforcement learning models. The importance of this dataset is that it can help develop smarter educational aids and improve students' ability to solve math problems. Product background information shows that the dataset was published by allenai on the Hugging Face platform, including two subsets of GSM8k and MATH, as well as IF Prompts with verifiable constraints, suitable for MIT License and ODC-BY license.
Demand population:
"The target audience is mainly educational technology developers, artificial intelligence researchers and data scientists. This dataset is suitable for them because it provides a large sample of mathematical problems that can be used to train and test the application of AI models in the field of education, especially in solving mathematical problems. In addition, it can help researchers explore how to use AI technology to improve students' learning efficiency and performance."
Example of usage scenarios:
Education software developers use this dataset to train AI models to automatically generate answers to mathematical problems
Researchers use data sets to analyze common mistakes students have when solving math problems
AI models provide personalized mathematical learning suggestions by learning questions and answers in the dataset
Product Features:
Contains two subsets of GSM8k and MATH, with a total of about 7500 mathematical problem samples
The IF Prompts subset contains 14,973 samples with verifiable constraints
Suitable for training reinforcement learning models, especially in the field of mathematical problem-solving
The dataset format is suitable for open-instruct and can be used to verify rewards
The types of problems are included, covering basic math to more complex mathematical problems
Datasets can be used to develop and test new educational technologies to improve educational efficiency
Suitable for research on how to improve students' math learning effectiveness through AI technology
Tutorials for use:
Step 1: Visit the Hugging Face platform and find RLVR-GSM-MATH-IF-Mixed-Constraints dataset
Step 2: Download the dataset and select the GSM8k, MATH or IF Prompts subset as needed
Step 3: Use datasets to train AI models, such as reinforcement learning models, to solve mathematical problems
Step 4: Verify and test the model using questions and answers in the dataset
Step 5: Adjust parameters according to the performance of the model and optimize the accuracy and efficiency of the model.
Step 6: Apply the trained model to a practical educational software or research project
