arXiv:2512.21002v3 Announce Type: replace Abstract: Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of reasoning data. However, knowledge distillation (KD) over lengthy sequences with prompt (P), chain-of-thought (CoT), and answer (A) sections makes the process computationally expensive. In this work, we investigate how the allocation of supervision across different sections (P, CoT, A) affects student performance. Our analysis shows that selective KD over only the CoT tokens can be effective when the prom
The increasing computational demands of large reasoning models and the push for more efficient AI development drive the need for novel distillation techniques.
Efficient reasoning distillation can significantly reduce the computational cost and resource requirements for deploying advanced AI capabilities, making them more accessible and scalable.
The method of distilling knowledge from large AI models to smaller ones can become much more efficient through targeted supervision on critical sections like Chain-of-Thought (CoT).
- · AI developers
- · Cloud providers
- · Edge AI manufacturers
- · Smaller AI companies
- · Companies reliant on brute-force large model deployment
Reduced computational costs for AI model deployment will increase the accessibility and breadth of advanced AI applications.
This efficiency could accelerate the development and adoption of AI agents by lowering their operational footprint.
More widespread and cost-effective AI could exacerbate existing economic and social challenges without careful regulatory and ethical considerations.
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