Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it

Opening excerpt

July 29, 2026 What a Distilled Model Inherits From Its Teacher Johnny Yu, Siddarth Mamidanna, Cyril Gorlla Introduction [ gpt-oss-20b-finance weights on Hugging Face ] [Try the playground ] [ LineageEval ] [Explore the data on GitHub ]

Reasons for distillation from Chinese open models include a perceived superior cost to performance ratio, as well as the notion that the potentially harmful aspects of its behavior will not transfer to the distilled model. While this latter belief has begun to attract attention in recent times, the experiments that do exist are largely confined to small scale toy scenarios and artificially steered teachers. We investigate this phenomenon in a practical setting: a frontier Chinese model, used as a teacher, in a finance-adjacent production distillation pipeline. It is well understood that Chinese frontier models visibly refuse and reframe China-sensitive topics; the behavior is documented across audits of the DeepSeek R1 and V3 lines. The question we seek to answer is one level removed: does the student learn undesired behaviors along with the skill?

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