Junlin Han

@han_junlin

Multimodal pre-training/post-training. Meta FAIR. PhD Oxford

London, England

junlinhan.github.io

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Junlin Han
@han_junlin
Aug 6
We live in a multimodal world. We see, talk, act, and dream. Yet most LLMs still start with language pretraining. Why not train them natively with multimodal I/O from scratch? Because it’s SUPER HARD, adding modalities triggers training instability, design complexity, and
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Junlin Han
@han_junlin
Aug 6
Thanks for posting our work! Hope this can be helpful for multimodal intelligence!
AK
@_akhaliq
Aug 6
Towards Physics of Multimodal Pretraining Knowledge Flow, Modality Synergy, Early Unification, and Recipes paper: huggingface.co/papers/2608.05…
Junlin Han
@han_junlin
Jul 7
Don’t miss Beyond Language Modeling with two amazing main authors @DavidJFan and @__JohnNguyen__ !!!!!
David Fan
@DavidJFan
Jul 7
@__JohnNguyen__ and I are presenting the Beyond Language Modeling paper as an ICML spotlight in 30 minutes at the 10:30 AM poster session! I’ll also be at the AMI Mixer on Thursday and hanging around in Seoul until early next week :)
Junlin Han
@han_junlin
Jul 3
best lecture/tutorial in native mllms!!!
Victoria X Lin
@VictoriaLinML
Jul 3
The video of my Stanford CS25 guest lecture, From Language Models to Native Multimodal Intelligence, is now online. I discussed how the core ideas behind LLMs has shaped multimodal AI, from architectures to training paradigms and scaling, and where the next challenges may lie.
Junlin Han
@han_junlin
Jun 24
💯 Very nice post, definitely worth reading!
Nan Liu
@nanliuuu
Jun 24
Part 2: Is 3D really necessary to understand the world? IMO, probably not. I always hear people say that since we live in a 3D world, we must use explicit 3D geometry. But I think we often confuse generating the world with understanding the world. Real understanding can actually
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