Jiaxuan Zou
Jiaxuan Zou is A guide to designing and building a Scaling Ladder, in execution order: decision objectives and acceptance, measurement protocol, scaling rules for dense and MoE, experiment matrix and budget, hyperparameter search,…. It is ranked #6553 on the AI Radar, in AI research, first seen 28 days ago and shared in 1 post (234 views).
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How to Build a Scientific Scaling Ladder | Jiaxuan Zou How to Build a Scientific Scaling Ladder Created on September 20, 2026 · -- views 2026 · scaling-laws pretraining hyperparameter optimization llm empirical-methodology · research 阅读中文原文 This article is compiled from public literature and technical reports and contains no confidential content . It covers how to build a Scaling Ladder: fit a Scaling Law through small-scale experiments, extrapolate the training configuration and performance of the target model, and use this to decide whether to launch the target training run. 1. Scope and Terminology 1.1 Definition of Scaling Ladder A…
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Jiaxuan Zou in numbers
- Rank on the AI Radar: #6553 of 10425
- Shared in 1 post by 1 account: @KakaluoteW45042
- 234 views on those posts
- First seen 28 days ago, last shared 28 days ago
- Pricing seen by AGK Intelligence: free
- Market: AI research
FAQ
What is Jiaxuan Zou?
A guide to designing and building a Scaling Ladder, in execution order: decision objectives and acceptance, measurement protocol, scaling rules for dense and MoE, experiment matrix and budget, hyperparameter search,… It was first shared on X 28 days ago and is ranked #6553 on the AI Radar.
Is Jiaxuan Zou free?
Yes, it is free to use.
Who shared Jiaxuan Zou?
1 account on X, including @KakaluoteW45042, in 1 post totalling 234 views.
Every post is read and classified by AGK Intelligence: what it is, which market it belongs to, and whether the link is a real tool. 65.3k posts from 5k X accounts over the last 14 days, 10.4k tools, 19 markets. New posts are collected several times an hour and the full ranking is recomputed every hour — last update 2026-11-06 17:44 UTC. Full method.