# 小模型+工具遇天花板，下一扩展轴是什么

- 来源：Dongxi 东锡 NLP (@dongxi_nlp)
- 发布时间：2026-08-18 03:40
- AIHOT 分数：41
- AIHOT 链接：https://aihot.virxact.com/items/cmsxnxga205vfroz08tm5j6bc
- 原文链接：https://x.com/dongxi_nlp/status/2089437336270488061

## AI 摘要

马东锡 NLP 引用 Jason Wei 观点，质疑“1B 认知核心+工具调用”范式：无需工具、快速自然地完成任务至关重要，参数化内化知识有信息上限，追求最高质量智能仍需更大模型。旧范式是预训练算力扩展，当前是推理时扩展，下一扩展轴成谜。

## 正文

This is illuminating, but it also raises a deeper question.

The old scaling paradigm:
More compute during pretraining -> smarter model

The current scaling paradigm:
More compute during inference -> better answer

The next scaling paradigm:
? -> ?

If small models + test-time scaling + tools are beginning to hit a ceiling, and the future shifts back toward much larger models that can solve more problems in a single forward pass, what exactly is the next scaling axis?

### 引用推文

> Jason Wei：When language models first started using tools well, I was sympathetic to the narrative that instead of scaling up language models, all we needed was a strong e...