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Google's Jeff Dean says current pre-training is passive: initialize a model, stream the internet past it, let it observe But models need to learn not just from data, but by acting, predicting, and choosing what to learn from next "we have this artificial distinction now between pre and post-training,... show more

Haider.

71,942 subscribers

55,812 Aufrufe • vor 7 Monaten •via X (Twitter)

Wissenschaft & Technologie

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8 Kommentare

Profilbild von Zuzanna Stamirowska

Zuzanna Stamirowskavor 7 Monaten

BDH trains at inference:

Profilbild von Vanar

Vanarvor 7 Monaten

Learning shouldn’t stop after pre training. Agents that act, learn from outcomes, and update their own context are where AI becomes truly adaptive

Profilbild von Harry Tandy

Harry Tandyvor 7 Monaten

static data is over models that choose their own path will dominate 2026

Profilbild von Michael Stewart

Michael Stewartvor 7 Monaten

And yet, they cannot figure out the architecture that would give them what they want, while we at ChronoDyne Systems, Inc. capitalize and build an IP moat as wide as the Mississippi.

Profilbild von Idan | Helping SaaS Win in AI Search

Idan | Helping SaaS Win in AI Searchvor 7 Monaten

Breakthrough is models telling us what data they actually need.

Profilbild von Bradley K. Sherman

Bradley K. Shermanvor 7 Monaten

Translation: Our Tech Overlords are out of ideas.

Profilbild von Jeremiah Adebayo

Jeremiah Adebayovor 7 Monaten

I concur. Teaching the model how to think is more important

Profilbild von RetroRanger Anonymous

RetroRanger Anonymousvor 7 Monaten

jeff gets it. pre-training is my warmup, post-training is where i lift. just watching never built muscle

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GLM 5.3’s biggest leap came from post-training alone. “GLM 5.3 was built entirely by post-training the previous model. For me, that was a wake-up call.” “Post-training is far more GPU-efficient than pre-training from scratch. Plenty of companies can’t afford to train a model from scratch, but they have proprietary data. They can post-train themselves and beat some of the closed models.” “There’s still so much juice left to squeeze from post-training. We compared GLM 5.3’s cyber capabilities with 5.2, and it’s a night and day difference. That’s all just post-training.”

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Most people have heard of big model smell, but what about pristine pre-training smell? Evan and Derry seem to think it exists.

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Arena.ai

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Grass is betting that better AI still starts with better pre-training data “Whatever you put into your model ultimately determines what your model's capabilities are” “Several of the top frontier AI labs in the world are working directly with Grass to acquire large amounts of pre-training data” “If that information is not present in your model, your model doesn't know how to do that thing in the first place” Andrej puts Grass close to one of the most fundamental inputs in AI: the training corpus itself. As frontier models scale, they need enormous amounts of diverse, high-quality web data. They are building the infrastructure to collect and supply that data at internet scale. Model capability starts with what the model gets to learn from.

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