It's my belief training was just the second major unhobbling on the way to agents. The model used for o1 and o3 is not particularly more intelligent than any other, it just has learned to think for longer. And with o3 it reinforced that, reasoning better for longer.

__The first unhobbling was the RLHF__ that allowed LLMs to follow instructions and made them chatbots. Both of these unhobblings massively improved the capabilities of the same size model. And reasoning seems to have a feedback loop as seen with o3.

> “Unhobbling” is what actually enabled these models to become useful—and I’d argue that much of what is holding back many commercial applications today is the need for further “unhobbling” of this sort. Indeed, models today are still incredibly hobbled!

_Aschbrenner: _ https://situational-awareness.ai/from-gpt-4-to-agi/

There are many more unhobblings to go. Some are:

- Tool use
- Large contexts (and better utilizations of those concepts) 
- Agents (working independently)
- Swarming (farming out with to other bots)

There's still a lot of headroom before we even get to changing architectures (which is happening).

Sonnet may be an excellent coder, but it still __never runs any of the code it writes.__ It just writes it all in one go and hopes it's right? What developer does that? o3 does the same and it's in the top 200 programmers in the world. What happens when chatbots get a terminal and can test and verify their own code? What happens when they can use VSCode? Or better yet they can use the entire computer and all the apps humans use?

I'm sure there are many promising new areas of research and algorithmic improvements, but there is low having fruit everywhere. 

My question is just by removing the shackles and allowing them to use the tools we use, do you think we may see a similar jump as from o1? What other things are holding current LLMs back?

Reasoning was just the Second Unhobbling...