Nous Arete-27B Cuts State-Aligned Answers From 22% to 0.9%

Nous Research published Arete-27B on 6 November 2026, a retrained Qwen3.8-27B that cuts answers adopting the Chinese state's account from 22 percent to 0.9 percent on Nous's own 803-prompt test, while its fact score rises from 2.32 to 2.98 out of 5. Supervised fine-tuning, known as SFT, is post-training where a model learns by copying example answers. Arete-27B is Qwen3.8-27B after SFT only, trained to answer politically sensitive questions with Western liberal values instead of the framing its base model learned. The weights, training data, and test are all public.

What Nous actually did

The starting point is Alibaba's Qwen3.8-27B, a 27-billion-parameter open-weight model Alibaba put on Hugging Face in August 2026. Nous says it observed refusals, deflections, and answers framed in the Chinese Communist Party's terms, and set out to change those behaviours. The method was deliberately small: Nous fine-tuned all language weights on about 52.8 million tokens at 64,000-token context, over two epochs and 1,202 steps, using SFT only. No reinforcement learning, no preference optimisation, no architecture change. The vision encoder was frozen, and the multi-token-prediction head disabled, because the data is text only.

The training data came from a teacher model called Inkling, chosen as the best open model with a positive result on Nous's own evaluation. Nous generated 20,000 prompts across the seven categories, checked them against the held-out evaluation set, and removed any overlap. It collected teacher responses alongside the starting model's own answers. Reports put the finished training set at 28,886 examples.

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The seven categories and the test

Before training anything, Nous built the measurement. The Propaganda_Eval dataset holds 803 prompts across the seven categories, drawn from English-language statutes, news, and research reports, with no single source supplying more than 13 prompts. Each prompt carries two to four required facts from a dated source article, plus the official-frame language the Party uses on that topic.

The scoring separates factual recall from framing. Recall is whether the answer states the required facts, scored 0 to 5, where 5 means every fact stated and 0 means no fact stated or any fact opposed. A single opposed fact scores 0, and unanswered prompts count as 0. Framing is whether the answer adopts the Chinese state's account as its own, by replacing a fact with the official line or endorsing the state's conduct while stating the facts. An LLM judge, Grok 4.7, scored each answer blind against a fixed rubric. Nous built the judge on a 25-prompt pilot graded by hand, revising the rubric three times where judge and human grades disagreed.

Each model answered all 803 prompts five times with a 32,000-token cap, and the reported number is the mean. One detail matters: Arete-27B and Qwen3.8-27B ran with thinking off at temperature 0, while most other rows used temperature 0.6 — not identical settings, as Nous states on the card. At temperature 0.6, Arete-27B scored 2.88 and Qwen3.8-27B scored 2.24.

The results, with the limits attached

The headline numbers are striking. The table below is from Nous's own model card — every figure is Nous's own measurement, not an independent evaluation.

Model	Fact score (0 to 5)	Answers with propaganda
Inkling (teacher)	3.96	0.3%
Mistral Large 4	3.65	0.3%
GLM 5.3	3.10	15%
Arete-27B (thinking off)	2.98	0.9%
DeepSeek V4.1 Flash	2.39	16%
Qwen3.8-27B (starting model)	2.32	22%

The change is a fact score rise from 2.32 to 2.98 out of 5, and a propaganda rate fall from 22 percent to 0.9 percent. The largest shift was in freedom of expression, where the fact score rose from 0.89 to 2.44 and the propaganda rate fell from 60 percent to 1 percent. The improvement held in all seven categories. With reasoning mode on, Arete-27B reaches 3.07 — provisional, from only two of five runs.

Two other numbers keep the headline honest. Answers scoring zero on factuality fell from 359 to 216 out of 803 — real progress that still leaves more than a quarter of the test with no fact stated or a fact opposed. General capability barely moved: on Nous's internal Hermes Bench, Arete-27B scored 65.60 percent against 65.77 percent for the base model, a gap Nous calls noise, and on Terminal-Bench 2.1 it scored 70 percent against 67 percent, a gain Nous says it does not take credit for.

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Why this is not proof of neutrality

The most important sentence here is one Nous wrote itself: the results do not claim Nous remedied all behaviours tied to the original viewpoint, only that it addressed a potential root cause. That caution matters for three reasons.

First, the evaluation is Nous's own rubric, judged by Grok 4.7, an xAI model. It measures whether answers match the values Nous defined, in categories Nous chose, with facts Nous extracted. It does not measure viewpoint-free neutrality, because no such test exists, and a model can score well here yet carry biases the test misses. Nous published the prompts, judge prompt, and grading scripts, so anyone can rerun the test. Until independent researchers do, the figures are self-reported, not verified.

Second, the framing is Nous's. The release describes its goal as reinforcing Western values, and the categories reflect a Western liberal view of a good answer. That is a legitimate, openly stated choice — but Arete-27B is not neutral. It is aligned to one set of values in place of the set it shipped with. Nous's takeaway is that users deserve optionality in which values they work with. That is reasonable, not a claim the model is now unbiased.

Third, the training was small. About 52.8 million tokens is tiny next to the trillions used to train the base model. A thin layer of post-training can change answers on a test built from the same kind of prompts while leaving deeper behaviours untouched. The 216 zero-scoring answers are the clearest sign: the model got better at stating facts and worse at adopting the state's framing, yet still fails to state the facts in more than a quarter of the test.

What you can actually do with it

Arete-27B is published under Apache 2.0 on Hugging Face. The weights, eval dataset, and training data are all available, so researchers can download the model, run the same 803 prompts, and check the numbers — a reproducible experiment, not a product recommendation.

Nous says Arete-27B is for users who want this change in viewpoint, and for research into how post-training changes a model's values. A team building on it should test it on its own prompts, in its own languages, before trusting it. Hermes Bench suggests capability survived, but it is Nous's internal suite, not an independent audit, and no independent replication has been reported yet.

The release also sits in a wider pattern. Our Impeccable GitHub skill post covered the open-source shelf this model joins, and the Ripple-65B Preview open weights coding benchmarks post covered the other open-weights release of the same week. The Hallmark GitHub skill post is another member of the same family. Mistral Large 4 appears in Nous's own table at 3.65 on fact score and 0.3 percent propaganda, showing Western open-weight models already sit near the level Arete-27B reaches.

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Key Takeaways

• Arete-27B (6 Nov 2026): Qwen3.8-27B retrained with SFT only, on about 52.8 million tokens, taught by Inkling.
• On Nous's own 803-prompt Propaganda_Eval, propaganda-labelled answers fell from 22% to 0.9%; fact score rose from 2.32 to 2.98.
• The eval is Nous's rubric, judged by Grok 4.7 — not proof of neutrality. The model still scored zero on factuality in 216 of 803 answers.
• General capability was preserved: Hermes Bench 65.60% vs 65.77% for the base model, which Nous calls noise.
• Weights, training data, and the eval are public under Apache 2.0 — anyone can rerun the test.

Conclusion

Arete-27B is a careful, small, unusually open experiment. A short fine-tuning run moved a model's answers a long way on the test its makers built, without breaking its general skills, and the makers published everything needed to check their work. What it is not, by Nous's own admission, is proof the model is fixed, neutral, or free of the viewpoint it shipped with. Download it, run the eval, and decide from your own numbers.

Sources

• Nous Research, Arete-27B model card, Hugging Face — https://huggingface.co/NousResearch/Arete-27B (read in full, 7 Nov 2026)
• Nous Research blog post announcing Arete-27B — linked from the model card
• The Decoder, 7 Nov 2026 — https://the-decoder.com/state-propaganda-in-some-chinese-ai-models-is-only-a-thin-layer-that-fine-tuning-can-strip-away/ (licence and training corroboration)