Honestly, not sure I would agree with this. Like Chollet said, this is fundamentally different from simply scaling the amount of parameters (derived from pre-training) that a lot of previous scaling discourse centered around. To then take this inference time scaling stuff, which requires a qualitatively different CoT/Search Tree strategy to be appended to an LLM alongside an evaluator model, and call it scaling is a bit of a rhetorical sleight of hand.
While this is no doubt a big deal and a concrete step toward AGI, there are enough architectural issues around planning, multi-step tasks/projects and actual permanent memory (not just RAG) that I'm not updating as much as much as most people are on this. I would also like to see if this approach works on tasks without clear, verifiable feedback mechanisms (unlike software engineering/programming or math). My timelines remain in the 2030s.
Honestly, not sure I would agree with this. Like Chollet said, this is fundamentally different from simply scaling the amount of parameters (derived from pre-training) that a lot of previous scaling discourse centered around. To then take this inference time scaling stuff, which requires a qualitatively different CoT/Search Tree strategy to be appended to an LLM alongside an evaluator model, and call it scaling is a bit of a rhetorical sleight of hand.
While this is no doubt a big deal and a concrete step toward AGI, there are enough architectural issues around planning, multi-step tasks/projects and actual permanent memory (not just RAG) that I'm not updating as much as much as most people are on this. I would also like to see if this approach works on tasks without clear, verifiable feedback mechanisms (unlike software engineering/programming or math). My timelines remain in the 2030s.