Grade School Math

Research on grade-school math problem-solving is intensely focused on evaluating and improving the capabilities of large language models (LLMs) to accurately and robustly solve these problems, revealing both surprising strengths and persistent weaknesses in their reasoning abilities. Current efforts investigate the impact of training data quality, including the presence of dataset contamination and the benefits of incorporating error-correction data, and explore different model architectures and training techniques to enhance performance, particularly for longer and more complex problems. These studies are crucial for understanding the limitations of current LLMs and for developing more effective educational tools and real-world applications that require mathematical reasoning.