Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it
We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's…
Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it launched on July 30, 2026 with 170 votes, #192 of 24,623 launches that month and more than 95% of that year's launches.
In plain words
This project uses knowledge distillation to transfer finance task expertise from DeepSeek V4 Flash into open-source language models. The resulting 120B model achieves 83.61% accuracy on financial reasoning benchmarks within an 8k token limit. The creators released a 20B open-weights version and investigated whether censorship characteristics transferred from the teacher model. Their findings indicate the distilled model retained its original behavior patterns rather than adopting the teacher's content restrictions. Users can test the model through an interactive playground.
written from the facts on this page · September 2026
From the sources
DeepSeek V4 Flash scores 45 points more censored on China-sensitive questions than on matched controls. Every model trained on it stays at the level of the untouched American base.
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…from ctgt.ai
In the maker’s words, at launch
We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the…
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