Model Distillation

Model distillation is a machine learning technique where a smaller, faster neural network (student model) learns to replicate the behavior of a larger, more complex model (teacher model). From a governance perspective, distillation matters because it enables organizations to deploy AI systems with reduced computational requirements while maintaining performance, which improves auditability, reduces operational costs, and makes compliance monitoring more feasible. This approach is particularly relevant for enterprises managing model transparency requirements and seeking to operationalize AI in resource-constrained environments without sacrificing the explanatory capabilities of their original systems.

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ResearchUS2026-07-30

Distillation Study Finds Censorship Does Not Transfer, But Supply Chain Risk Does

CTGT published empirical research on July 29, 2026 testing whether political censorship behaviors from DeepSeek V4 Flash transfer to a distilled student model through knowledge distillation for financial reasoning tasks. Using a 304-prompt evaluation framework called LineageEval with four independent LLM judges, researchers found the teacher model scored 45.45 points more censored on China-sensitive prompts than matched controls, while the distilled student showed no statistically significant censorship transfer. The findings do not eliminate AI supply chain risk from Chinese teacher models, but they do change what compliance teams need to assess and document.

DeepSeek model distillation AI supply chain risk censorship transfer model evaluation open-weight models financial services