I recently tested Gemma 4-31B locally and I was blown away with the intelligence/size ratio of this model. These papers show how they achieved such distillation capabilities.[R]

Reddit r/MachineLearning / 4/27/2026

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The article explains that Gemma distillation is more than standard next-token training, using a teacher model to provide detailed “thought process” information for each word.
The student model learns from the teacher’s full probability distributions, giving it richer supervision than it would receive by training alone.
This coaching enables the resulting smaller distillation models to outperform significantly larger models, improving intelligence-to-size efficiency.
The post points readers to a related collection of papers and notes that Gemma 4 applies a similar underlying approach, with a stronger teacher model (3.1 Pro) improving results.

The secret sauce here is that the student model does not just try to guess the next token in a sentence, which is how most AI is trained. Instead, the teacher model shares its entire "thought process" for every single word. It gives the student a detailed probability distribution, which is rather counterintuitive if you want to build something smaller! This gives the student much "richer" information at every step and allows it to learn way more efficiently than it could on its own. Because of this intense coaching, the Gemma distillation models can beat models that are significantly larger.

Go through this papers collection that I shared and you can get a better understanding of how it works. [Content from before Gemma 4, but they're using the same underlying approach for Gemma 4 as well. It's just that the teacher (3.1 Pro) is better now]

https://app.7scholar.com/shared/9dca3315-36d1-40ce-bee2-cf6922c0136c/Q707uXeQjQ70

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