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And also "instilled during their reinforcement training", and we are currently pushing planning hard there, for autonomous agents.

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No I think reinforcement training would be an example of not innate. Don't you? That's like potty training.

Is it? Both supervised learning and reinforcement learning are ways of training the model, and the difference between them is not that big. I would say that innate means "in the weights", while non-innate means things the model learned during inference, during its "lifetime".

Maybe you're right. In the weights might be the right way to frame that. What do you mean by "during its lifetime"? Do you mean things like system prompts or things in Claude.md?

It sounds like you're framing a session as a "lifetime". Whch might be right, I haven't thought of it like that before though. So when I /compact my session what's that even the equivalent of I wonder.

> Do you mean things like system prompts or things in Claude.md?

All of it - system prompts, user prompts, few-shot examples, Claude.md, things that an agent learned by exploring its environment...

> So when I /compact my session what's that even the equivalent of I wonder.

Sleep? :)

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