2001zhaozhao

I find the part about extreme specialization very interesting, and potentially applicable to training AI agent systems (from an outsider's perspective). Today's instruction-following LLMs could in theory cooperate since they don't yet follow goals outside of their prompt, so we can just prompt them to work together with each other and they will do so without hesitation. So it sounds like we can get a lot of benefit from specialization if we can train them to cooperate effectively.

Today's frontier LLMs are quite general-purpose and benefit from being so, and I would guess that's both for economic reasons during training (one big frontier model outperforms many smaller specialized models for the same training... (read more)

I noticed that Claude Code is much more likely to print out some short message response to the user before using each tool when reasoning is off compared to when it's on. Something to the effect of "Let us continue to [do the next step in solving the current problem]..."

I wonder whether part of this behavior can be explained by Claude wanting more "time" to reason silently under the scenes when producing its output. This post is about the AI "thinking" while processing the input tokens, but I think a lot of opaque reasoning might also be happening while the model is generating its output, even if it is generating unrelated tokens,... (read more)