With the new class of reasoning models, we have computerized both System 1 and System 2.

System 1 - Automatic, cheap, parallel retrieval of past experiences.

System 2 - Effortful, expensive, serial creation of novel reasoning.

It looked like they were able to reason, but that was due less to them being capable of reasoning and more due to their planetary scale.

If you thought of them as just like a normal single human, you'd miss the fact that they are a totally different category: a planetary-scale hive mind of vibes and memories.

LLMs can quickly retrieve past vibes and tweak them to fit a novel pattern, but only a little bit.

But with planetary scale, it doesn't really matter–there is such huge coverage of scenarios that there's likely a pretty good one to draft off of.

As a result, we tricked ourselves into thinking they could do novel reasoning when really what they were doing is cache retrieval with a bit of augmentation.

This is why we got results like this one showing that the previous class of models couldn't do compositional tasks^[aab]–its faux reasoning wasn't powerful enough.

The System 1 has enough coverage that if you give it the time and space to think step by step, it can do proper reasoning even for truly novel scenarios not captured directly in the System 1.

It has humans-style common knowledge it can use to reason its way through novel scenarios.

These models also give a reinforcement learning style curve of quality for that reasoning ability to then climb up.

Turns out that computers need time to reflect and think to get better answers, just like humans do!

The computer System 1 is orders of magnitude beyond any human's System 1 that has lived or could ever live.

The computer System 2^[aad] is currently high school graduate level.