Learning to Solve Complex Problems via Dataset Decomposition
arXiv:2602.20296v1 Announce Type: new Abstract: Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult tasks. We propose a novel scoring system to measure data difficulty based on its structural complexity and conceptual depth, allowing curriculum construction over decomposed data. Experiments on math datasets (MATH and AIME) and code generation datasets demonstrate that models trained with curricula generated by our approach exhibit superior performance compared to standard training on origina
arXiv:2602.20296v1 Announce Type: new Abstract: Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult tasks. We propose a novel scoring system to measure data difficulty based on its structural complexity and conceptual depth, allowing curriculum construction over decomposed data. Experiments on math datasets (MATH and AIME) and code generation datasets demonstrate that models trained with curricula generated by our approach exhibit superior performance compared to standard training on original datasets.
Executive Summary
This article proposes a novel approach to curriculum learning, where complex datasets are recursively decomposed into simpler components. A teacher-student framework is introduced, enabling the teacher to reason step-by-step and generate easier versions of examples, allowing the student model to progressively master difficult tasks. A novel scoring system measures data difficulty based on structural complexity and conceptual depth, facilitating curriculum construction over decomposed data. Experiments on math and code generation datasets demonstrate superior performance of models trained with curricula generated by this approach compared to standard training. The method has the potential to improve the efficiency and effectiveness of machine learning model training, particularly for complex tasks.
Key Points
- ▸ Dataset decomposition is used to create simpler, more learnable components of complex datasets.
- ▸ A teacher-student framework is introduced, where the teacher reasons step-by-step to generate easier versions of examples.
- ▸ A novel scoring system measures data difficulty based on structural complexity and conceptual depth.
Merits
Improved Efficiency
The approach enables models to learn complex tasks in a more efficient manner by progressively mastering difficult tasks through curriculum learning.
Enhanced Model Performance
Experiments demonstrate superior performance of models trained with curricula generated by this approach compared to standard training.
Flexibility and Adaptability
The method allows for the creation of personalized curricula tailored to the specific needs and goals of the model.
Demerits
Scalability Limitations
The approach may not be scalable to extremely large or complex datasets, requiring significant computational resources and time.
Data Quality Requirements
The method relies on high-quality data and may struggle with noisy or incomplete data, requiring additional preprocessing or curation.
Teacher-Student Framework Complexity
The teacher-student framework introduces additional complexity, requiring careful design and implementation to ensure effective learning.
Expert Commentary
This article presents a novel and promising approach to curriculum learning, which has the potential to revolutionize the field of machine learning. By recursively decomposing complex datasets into simpler components, the teacher-student framework enables models to learn complex tasks in a more efficient and effective manner. The method has been demonstrated to improve model performance on math and code generation datasets, and its implications for artificial general intelligence are substantial. However, the approach is not without its limitations, including scalability and data quality requirements. Nevertheless, the method has the potential to make a significant impact on the field of machine learning and beyond.
Recommendations
- ✓ Future research should focus on scaling the approach to larger and more complex datasets, as well as exploring its applications in other domains, such as natural language processing and computer vision.
- ✓ The method should be further evaluated and refined to ensure its effectiveness and efficiency in real-world scenarios, including the development of tools and frameworks to support its implementation.
