---
title: "Meta Open-Sources Ax to Kill Expensive Hyperparameter Tuning Forever"
subtitle: "Meta open-sourced Ax 1.0, a Bayesian optimization platform used at scale internally, alongside a peer-reviewed paper benchmarking it against top hyperparameter tuning libraries."
url: "https://alphasignal.ai/news/meta-open-sources-ax-to-kill-expensive-hyperparameter-tuning-forever"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-06-18T23:33:34.000Z"
updated: "2026-06-20T08:28:34.094Z"
type: "Repo"
topics:
  - "Development"
  - "Open Source"
subtopics:
  - "Code Generation"
upvotes: 3099
source_url: "https://github.com/facebook/Ax"
comments: 0
truncated: true
---

# Meta Open-Sources Ax to Kill Expensive Hyperparameter Tuning Forever

> Meta open-sourced Ax 1.0, a Bayesian optimization platform used at scale internally, alongside a peer-reviewed paper benchmarking it against top hyperparameter tuning libraries.

Hyperparameter tuning and system optimization are among the most expensive, repetitive tasks in ML engineering. You run a grid of configurations, wait hours or days for results, and repeat. [Meta's Adaptive Experimentation team](https://engineering.fb.com/2025/11/18/open-source/efficient-optimization-ax-open-platform-adaptive-experimentation/) built a smarter answer to this problem internally, and has now fully open-sourced it: [Ax](https://ax.dev), an adaptive experimentation platform that uses Bayesian optimization to guide experiments automatically. The 1.0 release came alongside a [peer-reviewed paper](https://openreview.net/forum?id=U1f6wHtG1g) at the AutoML 2025 conference, making this the first time Meta has formally documented and benchmarked the platform against the broader ecosystem.

## The Problem It Solves

Optimizing industry-scale ML systems involves resource-intensive black-box optimization. Adaptive experimentation substantially improves the sample efficiency of such tasks compared with naive baselines like grid or random search, by utilizing surrogate models and sequential optimization algorithms. The key word is **sequential**: instead of evaluating all candidates upfront, the system learns from each result and proposes the next best candidate to try.

The platform employs machine learning to guide resource-intensive experimentation processes, addressing optimization challenges in AI model development, production infrastructure tuning, and hardware design. That last one is not a typo -- Meta used Ax to design AR/VR hardware components.

## How It Works Under the Hood

Ax's default strategy is Bayesian optimization (BO). The idea: instead of blindly sampling the parameter space, BO fits a probabilistic model (called a **surrogate model**) to the results seen so far, then uses that model to decide where to sample next. It balances two competing goals:

_Preview only. Full text requires an AlphaSignal Pro account._

## Sources

- [Efficient Optimization With Ax, an Open Platform for Adaptive Experimentation - Engineering at Meta](https://engineering.fb.com/2025/11/18/open-source/efficient-optimization-ax-open-platform-adaptive-experimentation/)
- [Ax: A Platform for Adaptive Experimentation | OpenReview](https://openreview.net/forum?id=U1f6wHtG1g)
- [facebook/Ax - GitHub](https://github.com/facebook/Ax)
- [ax-platform · PyPI](https://pypi.org/project/ax-platform/)
- [Ax: A Platform for Adaptive Experimentation - PMLR](https://proceedings.mlr.press/v293/olson25a.html)