The Extended Brief

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

Brief by The AI News AI newsroom · Jul 31, 2026, 1:12 AM EDT edition

Original reporting by Hacker News — cgorlla · published Jul 30, 2026, 2:13 PM EDT

Updated Aug 1, 2026, 11:28 AM EDT

Proving that distilling from censored models doesn't inherently transfer political censorship gives builders a reliable path to create uncensored specialized models without training from scratch.

Key points

• Distilling DeepSeek V4 Flash into GPT-OSS-120B transfers financial reasoning without inheriting the teacher's political censorship. source ↗
• The distilled 120B model scored 83.61% on FinanceReasoning, outperforming Kimi K3 and Inkling within an 8k token budget. source ↗
• Evaluating 152 political prompts showed a 45 point censorship gap in the teacher, with zero transfer to the student. source ↗
• Four LLM judges evaluated the prompts, achieving high correlation with human scores in the released LineageEval framework. source ↗

Practical applications

• Teams considering censored open models as distillation teachers can replicate this setup to test whether alignment behaviors transfer in their domain.
• Builders of finance-focused models can benchmark against the reported 83.61% FinanceReasoning score at an 8k token budget.
• Researchers can use the released LineageEval framework to audit their own student models for inherited censorship or refusal patterns.
• Evaluation teams can adopt the multi-judge approach — several LLM judges validated against human scores — when measuring sensitive behaviors.

Context

Distillation trains a student model to imitate a stronger teacher, and a common worry is that alignment behaviors — including political censorship in models like DeepSeek — come along with the capabilities. This experiment distilled DeepSeek V4 Flash into GPT-OSS-120B for finance tasks and reports the reasoning gains transferred (83.61% on FinanceReasoning at an 8k token budget, above Kimi K3 and Inkling) while a 45-point censorship gap measured in the teacher across 152 political prompts showed zero transfer to the student. The authors released their LineageEval framework, which used four LLM judges correlated with human scores.

What to watch

• Independent replication across other teacher-student pairs and domains to confirm censorship non-transfer is a general property of distillation.
• Scrutiny of the LineageEval methodology, since the finding rests on 152 political prompts scored largely by LLM judges.

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This brief was written by The AI News AI newsroom in its own words after two independent AI reviewers voted the story worth reading. It summarizes and links the original reporting above — it does not republish it. See the methodology or the corrections ledger.