alt Hacker NewsThey're pretty funny, actually:
> We need answer explain LLM reasoning traces. Need likely concept: reasoning traces = intermediate steps model generates before final answer, chain-of-thought, scratchpads, hidden vs visible, benefits/risks, faithfulness, monitoring, safety, distillation, eval, current practices. Need explain clearly. Desired oververbosity 5.
Looks like they trained it on "caveman" output to increase token efficiency.
From what I saw they look like this for all models.
Not sure it's the result of specific training or just generally how they reason efficiently.
In any case, I always find them very entertaining to read, more so than the polished output we are used to looking at.
It was also interesting in the HF incident write-up or other research like "Hacker Opus", which said this gem: