# Google DeepMind 推免训练递归架构新论文

- 来源：elvis (@omarsar0)
- 发布时间：2026-08-23 23:29
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmt5zer8q0uvfro73it37ymdr
- 原文链接：https://x.com/omarsar0/status/2091548272968245466

## AI 摘要

Google DeepMind 提出一种免训练方法，通过让模型自身指导架构修改来进化模型架构，或可启发更稳健的递归自我改进。该方法在推理时引入“再循环”机制，将激活反馈回模型自身，无需重新训练即可追踪信念状态，生成成本保持平稳。在 Gemma3 系列上，自适应变体将困惑度降低 23%，GSM8k 准确率提升 21%，且原始权重冻结。

## 正文

You don't often see one-word titles in AI papers.

That aside, strong recommend this paper from Google DeepMind.

I think this is an interesting training-free approach to evolve model architectures by leveraging the model itself to inform architectural modifications.

Something like this could also inspire even more robust recursive self-improvement approaches.

Approach details below:

A feedforward transformer can only update its internal state as many times as it has layers. Long generations need more updates than that, so chain-of-thought ends up doing basic state tracking in text.

Recirculation adds recurrence at inference time.

The model feeds activations back through itself during prefill, which lets it act like a dynamical system and track belief states without any retraining.

Generation cost stays flat. All the serial work happens in prefill.

On the Gemma3 family, the adaptive variant cuts perplexity 23% and lifts GSM8k accuracy 21%, with the original weights frozen and only light hyperparameter tuning.

Paper: https://arxiv.org/abs/2608.17981

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