Adaptive Knowledge Distillation for Efficient Reasoning in Compact Language Models
Keywords:
adaptive knowledge distillation, compact language models, efficient reasoning, model compression, socio-technical infrastructure, governance, sustainabilityAbstract
The increasing computational footprint of contemporary language models has created a pressing need for compact models that can perform sophisticated reasoning tasks under restrictive deployment conditions. This paper examines adaptive knowledge distillation as a systems-level strategy for transferring reasoning behaviors, calibration signals, and control knowledge from large teacher models into smaller student architectures. The analysis extends beyond conventional accuracy-oriented compression by integrating architectural design, infrastructure planning, robustness, fairness, governance, and sustainability. Adaptive distillation is understood as a continuous coordination process in which the student model receives teacher demonstrations, intermediate reasoning states, or feedback according to task difficulty, uncertainty, and operational constraints. The paper presents a conceptual framework that connects scaling trends, architectural bottlenecks, and deployment trade-offs. It discusses how adaptive mechanisms can reduce latency, memory, and energy consumption while preserving inferential coherence. The governance dimension includes evaluation asymmetry, bias transmission, auditability, and the distribution of accountability across developers and operators. Case-oriented comparisons across cloud, edge, and regulated environments illustrate the structural choices involved. The conclusion outlines a research agenda for integrating adaptive knowledge distillation into responsible AI procurement, benchmarking, and policy. The discussion is deliberately qualitative and systems-oriented, avoiding algorithm-level formalization in favor of interdisciplinary synthesis.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
