Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility
arXiv:2605.29229v1 Announce Type: new Abstract: Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the training data align with the student model. This paper introduces the Data-Model Compatibility (DMC) metric, which can be used to assess the suitability of a dataset for reasoning distillation on a student model. DMC provides an assessment by jointly considering data quality, relative difficulty, and student capability. We validated the effectiveness of DMC from two perspectives: (1) DMC exhibits
The proliferation of LLMs and the increasing demand for efficient AI deployment drive the need for better reasoning distillation techniques to optimize resource usage.
This development offers a method to more effectively transfer complex AI capabilities to smaller, more resource-efficient models, impacting the scalability and accessibility of advanced AI.
The introduction of the Data-Model Compatibility (DMC) metric provides a standardized and validated way to assess training data suitability for reasoning distillation, improving efficiency and performance.
- · AI developers
- · Smaller AI model providers
- · Organizations with limited compute resources
- · Inefficient AI training methodologies
- · Organizations over-relying on large, costly models without optimization
More effective and ubiquitous deployment of advanced reasoning AI capabilities.
Reduced computational costs for deploying sophisticated AI, broadening access to advanced AI applications.
Acceleration of AI integration into specialized edge devices and applications due to improved model efficiency.
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Read at arXiv cs.AI