[khafra]

"LLM whisperer" folks will confidently claim that base models are substantially smarter than fine-tuned chat models; with qualitative differences in capabilities. But you have to be an LLM whisperer to get useful work out of a base model, since they're not SFT'ed, RLHF'ed, or RLAIF'ed into actually wanting to help you.

[andai]

How can I learn more about this?

Is it like in the early GPT-3 days, when you had to give it a bunch of examples and hope it catches the pattern?

[nullc]

Not so much examples, though those can help... but you have to imagine a document of a sort that would be in the training set whose completion would be the answer you seek.

Like, "Solve this equation for me: " more likely gets completed with "Do your own homework buddy!" or just a list of more similar questions without answers. While, "careful analysis revealed the solution the equation X turned out to have a solution of", might be more likely to get what you want.

Also a lot more sensitivity to tone and context, write a prompt that sounds like it was written on some teenager fan subreddit, you'll get an answer of the sort that sounds like it belongs there.

[im3w1l]

Back in those days I would either create a little scene with a knowledgeable person and someone with a question. Or I would start writing a monologue and generate a continuation for it.

[Der_Einzige]

Me being old man yelling at cloud about how your chat/tool template matters more than your post-training technique.

DeepSeek-R1 is trivially converted back to a non reasoning model with just chat template modifications. I bet you can chat template your way into a good quality model from a base model, no RLHF/DPO/SFT/GRPO needed.