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
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.
- The distilled 120B model scored 83.61% on FinanceReasoning, outperforming Kimi K3 and Inkling within an 8k token budget.
- Evaluating 152 political prompts showed a 45 point censorship gap in the teacher, with zero transfer to the student.
- Four LLM judges evaluated the prompts, achieving high correlation with human scores in the released LineageEval framework.
From the source
“Across 152 matched prompt pairs scored by four judges from four different American frontier labs, DeepSeek V4 Flash scored 45.45 points more censored on China-sensitive questions than on structurally identical non-China controls.”
“An American model distilled on financial reasoning from DeepSeek V4 Flash displayed no statistically significant difference in behavior from the untouched base model.”
“Distillation from a censored teacher does not change student behavior on unrelated domains.”
“The judges were validated against 96 human-scored responses handpicked during rubric calibration: Pearson r of 0.948, mean absolute error 6.08 points, within 10 points of the human score on 81.3% of responses.”
“For a scoped task with a pragmatic latency and token budget, a 120B that finishes is worth more than a 2.8-trillion-parameter model that truncates.”
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.
Who should care
ML researchers and engineers who distill from open-weights models, and teams worried that using Chinese-trained teachers could import political censorship into their products.
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.
Editorial score 4.4 / 5 · significance 4.0 · novelty 5.0 · edge 3.5 · perspective 5.0
Desks: Research · Engineering · Tags: research, models, tooling
Evidence basis: Reviewed from a feed excerpt
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