Alex Hägele

@haeggee

PhD Student in ML @ICepfl MLO. MSc/BSc from @ETH_en. Previously: Fellow @AnthropicAI, Student Researcher @Apple MLR.

Lausanne, Switzerland

haeggee.github.io

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Alex Hägele
@haeggee
Jun 15
New research from our MLO Lab @EPFL: Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors. Magnitude-Direction Decoupling (MD): a simple optimizer tweak, and what we (currently) think is the right way to train efficiently at scale. 🧵
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Alex Hägele
@haeggee
Aug 20
personal update: a few weeks ago I joined DeepMind as a student researcher in the paris office, hosted by the great @m_e_sander in the Frontier Gemma team :)
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Alex Hägele
@haeggee
Jul 7
Come join our cracked team of researchers and engineers to work on pretraining safe models from scratch!
Bob West
@cervisiarius
Jul 6
🚨 3 AI Research Engineer positions @EPFL_en on safety pretraining & alignment for LLMs Co-hosted by #SwissAI #MLO #dlab ➡️ Will contribute directly to #Apertus—one of the world's largest fully-open LLM efforts, trained on 10k+ GPUs 🖥️ 👉 Info & app: dlab.epfl.ch/2026-07-06-saf…
Alex Hägele
@haeggee
Jul 6
i am also at #ICML2026 in Seoul 🇰🇷. hmu if you wanna chat :)
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Alex Hägele
@haeggee
Jun 25
Our paper is now on arXiv: arxiv.org/abs/2606.25971 Besides all the details and discussions of the broader literature, it also contains lots other experiments that answer some of the questions we have already received. For example:
Alex Hägele
@haeggee
Jun 15
New research from our MLO Lab @EPFL: Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors. Magnitude-Direction Decoupling (MD): a simple optimizer tweak, and what we (currently) think is the right way to train efficiently at scale. 🧵
4