Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it
Researchers distilled the outputs of the censored Chinese model DeepSeek V4 Flash into an American open‑source model, GPT‑OSS‑120B, to improve financial reasoning capabilities. The resulting model achieved 83.61% on the FinanceReasoning benchmark, outperforming Kimi K3 and Inkling while incurring significantly lower query costs. Evaluation across matched prompt pairs found no statistically significant transfer of political censorship to the distilled model.
- ▪The GPT‑OSS‑120B model was trained on outputs from DeepSeek V4 Flash to boost financial reasoning performance.
- ▪It scored 83.61% on FinanceReasoning, exceeding Kimi K3 (81.93%) and Inkling (65.13%) with 62‑times lower cost per query than Inkling and 160‑times lower than Kimi K3.
- ▪Judges found no significant difference in censorship behavior between the distilled model and the original base model.
- ▪Self‑distillation using the model’s own corrected continuations matched the performance of distillation from the Chinese teacher model.
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| Original publisher | Ctgt |
| Canonical URL | https://www.ctgt.ai/research/distillation-censorship-transfer |
| Publication time | Thu, 30 Jul 2026 18:13:06 +0000 |
| Retrieval time | 2026-07-30T19:57:35.505Z |
| Last seen | 2026-07-30T19:57:35.505Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | SNGpDW-c61x4 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
[gpt-oss-20b-finance weights on Hugging Face] [Try the playground] [LineageEval] [Explore the data on GitHub] +45.45 DeepSeek V4 Flash censorship gap on China-sensitive prompts vs matched controls · 76 pairs · four judges 83.61% CTGT GPT-OSS-120B on FinanceReasoning at 8k budget · above Kimi K3 at 81.93% and Inkling at 65.13% 62× Lower cost per query than Inkling at the same budget · 160× lower than Kimi K3 The affordability and accessibility of open frontier models has led to their widespread usage among American developers and enterprises. While this has enabled the benefits of AI to be reaped by more people, concerns have mounted over models influenced by foreign actors, namely the Chinese Communist Party.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ctgt.
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