TL;DR
We present the first zeroth-order method that is competitive with backprop at pretraining transformer language models. Dust perturbs activations (node perturbation) independently at every token, so each token is a
virtual
population member and one forward pass evaluates them all in parallel.
Dust approximates backprop closely at large population (i.e. substantially more compute) and in multiple settings even
exceeds
it. This hints that in a compute-rich regime we might be able to surpass backprop.
Dust is orders of magnitude more efficient than weight-space ES. From 1M tokens up, Dust is on the order of $10^3$ to $10^4$ times more efficient than a transformer implementation of EGGROLL, a state-of-the-art ES method, based on our extrapolations.
Zeroth-order methods are widely believed (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://qlabs.sh/research/dust)获取最准确的信息。

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🔗 **原文链接**: [Dust: Pretraining Transformers Without Backpropagation](https://qlabs.sh/research/dust)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 76票 · 👤 E-Reverance

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-10-06 08:01