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Current LLM architecture doesn't learn - and you're right this is a huge piece that normal folks fail to understand, since in many ways, it's the opposite of what years of AI research has been trying to create.

However, I think it's important to remember that LLMs are embedded in larger systems, and those larger systems do learn.

by lukewarm70729 minutes ago|

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exactly like you said - the harness might learn.

we do also have training on synthetic data. it might compound.

by timschmidt3 hours ago|

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They can form new associations between concepts via their input prompts and thinking text. That is a form of learning. Just not very durable. I liken it to https://en.wikipedia.org/wiki/Anterograde_amnesia

by illiac7863 hours ago|

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yeah, I should have been more specific: I meant the type of learning that mentoring fosters, the long term learning.

by timschmidt3 hours ago|

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I hear you. I think we are already seeing some middle ground with agentic systems using RAG, skills.md files, etc. It's a sort of disassociated card catalog memory. An engineer's notebook. Not the integrated, correlated, pre-processed set of relationships in the model. How to go backward from the notebook -> model cheaply without tanking performance is definitely one of those billion dollar questions.

by freedomben2 hours ago|

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I mostly agree, though after a mentoring session you can ask it to write skill or a memory and it can be reasonably durable. For Claude at least, the memories work pretty well (though I am still at a small scale with them. As they grow it might start to break somewhat. Doesn't always work, but has often enough that I thought it worth a mention.

by stingraycharles1 hours ago|

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> Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn.

I think this is a bit pedantic. Obviously the parent you’re replying to is referring to the concept of “in-context learning”, which is the actual industry / academic term for this. So you feed it a paper, and then it can use that info, and it needs steering / “mentoring” to be guided into the right direction.

Heck the whole name of “machine learning” suggests these things can actually learn. “reasoning” suggests that these things can reason, instead of being fancy, directed autocomplete. Etc.

In other news: data hydration doesn’t actually make your data wet. People use / misuse words all the time, and that causes their meaning to evolve.

by kasey_junk1 hours ago|

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I agree it’s pedantic and personally don’t get bent out of shape with people anthropomorphizing the llms. But I do think you get better results if keep the text prediction machine mental model in your head as you work with them.

And that can be very hard to do given the ui we most interact with them in is a chat session.