# Looped Flows：循环模型推理新训练法

- 来源：elvis (@omarsar0)
- 发布时间：2026-09-13 00:14
- AIHOT 分数：39
- AIHOT 链接：https://aihot.news/items/cmtyme1rk02mtronvtyr0oo17
- 原文链接：https://x.com/omarsar0/status/2098807354343260366

## AI 摘要

新论文提出 Looped Flows，用局部去噪目标训练循环架构，让每次隐藏状态更新彼此关联，解决以往梯度只流经最后几步、早期更新学不到东西的问题。作者称该模型在 6 个推理基准中的 5 个上优于此前的循环模型。推理时模型沿概率流运行，更细的时间网格可换取更多算力，不同起始噪声在存在多解的任务上可产生不同有效答案。

## 正文

Interesting paper to improve recurrent reasoning.

Looped models are great because you get more reasoning out of a model without adding parameters.

So this work proposes a looped architecture with a new training method.

The authors report wins over prior looped models on five of six reasoning benchmarks.

More details from the paper:

Looped models reason by updating a hidden state again and again at inference time. The hard part is training. Gradients usually flow through only the last one or two updates, so the early updates never learn to set up the later ones.

Looped flows train the recurrence with local denoising objectives, the way flow models are trained. Noise levels decrease step by step and share the same noise sample, which ties each update to the next.

At inference the model follows a probability flow. A finer time grid spends more compute, and different starting noise can produce different valid answers on tasks with more than one solution.

Paper: https://arxiv.org/abs/2609.11801

Chat with Paper: https://academy.dair.ai/papers/thinking-with-looped-flows-2609.11801