# NaceAI 超网络知识注入：不改大模型参数也能学新知识

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-24 05:27
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmry1sfmp0032row4efxe12ph
- 原文链接：https://x.com/rohanpaul_ai/status/2080404518957895722

## AI 摘要

NaceAI 研究提出一种超网络方法，让一个模型学习为另一个模型生成知识更新，无需修改基础模型的数十亿参数。该方法通过添加低秩矩阵来注入新知识，并重定向模型内部计算，将知识注入变为可复用流程。实验表明，目标模型越大，生成的更新泛化效果越好。

## 正文

New paper from @NaceAI shows a possible way to add large bodies of knowledge without rewriting the language model's core parameters.

The study advances a key continual-learning goal： teaching a model new facts without directly disturbing its existing parameters.

i.e. the method may reduce the need to repeatedly alter billions of base-model parameters whenever knowledge changes.

The big idea is that one model can learn how to manufacture knowledge updates for another model.

The base model's weights never move， but extra low-rank matrices are added alongside them. Those matrices carry the injected information and redirect the model's internal computation toward answers supported by the facts.

Knowledge injection here becomes a reusable process rather than a separate fine-tuning job for every factual corpus.

The method separates storing new facts from changing the language model that must reason with them.

A frozen language model may absorb new knowledge through generated adapters while preserving its original parameters.

The hypernetwork acts like a compiler that converts facts into temporary changes inside another model.

The strongest result is that these generated updates generalized better as the target language model became larger.

### 引用推文

> Nace AI：Releasing new research on HyperNetworks @ NaceAI Research Team. Introducing 📄Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models. ...