CoT wasn't pulled out of a hat. It was obvious to any researcher that it was needed. It just took some time and stronger base models and longer context to actually be implemented. People were already doing "CoT" by telling models to think step by step before answering, primitive harnesses and similar tricks.
Even a nobody like me was playing with a sort of CoT with llama finetunes before o1 came out.
Only this year I'd say we picked all the low hanging fruit that has been obvious for a while that was needed.
Hi, I am curious, what do you think are the parts that are obvious but still work in progress? Maybe continuous multimodal input?
I'm not fully up to date on AI research anymore, however I think the obvious stuff has already been implemented, but there's still a lot of room for improvement and scaling for these current approaches.
For verifiable tasks like coding and math, you can keep going, doing more and more RL. You have the AI generate hard problems, then try to solve them and reward correct solutions. We basically have RSI for verifiable tasks, we just need to keep the wheel turning.
Re:The End of AI Frontiers
by throw8383993 on Sunday November 08, 2026 @07:36AM
(#50412727)
I would take it from other angle.
Meta AI is actually frontier lab now with LLMs. Muse Wonderfull is usable with multi agent orchestration (where other labs struggle).
I do not think that would happen with pure research and without single focus!
You're being uncharitable. He would have been right if Chain of Thought hadn't been pulled out of a hat, and no one saw it coming.
At this moment no one can know if there will be a future step-change on LLMs similar to reasoning, and conversely, no one can be absolutely sure there won't be - so it shouldn't be a firable offense because it's not obvious; even though it looks like it in hindsight.