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I think long-term just having a single LLM responsible for everything will win out compared to brittle and complex subagent hierarchie. Most use of "subagents"

by krackers 11mo ago

I think long-term just having a single LLM responsible for everything will win out compared to brittle and complex subagent hierarchie. Most use of "subagents" today are just workarounds for LLM limitations: lack of instruction following, context length, non- determinism, or "hallucinations". All of these are things that will need to be solved long-term in the model itself though, at least if the AI bubble needs to be kept alive. And solving those things would in fact materially improve all sorts of benchmarks, so there's an incentive for frontier labs to do it. I think this is why you have the back-forth pattern that GP mentioned. You start with a single model doing everything. Then you find all sorts of gaps that you start to plug ad-hoc, and decide that breaking it into subagents might help fix things. This works for a while but then you realize that you lose out on the flexibility of a single-model having access to the entire context, so you starting trying to improve communication between subagents. But then a new model drops that fixes a lot of the things you originally had to workaround, so you go back to a single-model setup. Rinse and repeat. It's a great VC- bubble funded employment program though.

theptip 11mo ago
The general pattern seems to be that LLM+scaffolding performs better than LLM. In 6 months time a new model will incorporate 80% of your scaffolding, but also will enable new capabilities with a new layer of scaffolding. I suspect the model that doesn’t need scaffolding is simply ASI, as in, the AI can build its own scaffolding (aka recursive self-improvement), and build it better than a human can. Until that point, the job is going to remain figuring out how to eval your frontier task, scaffold the models’ weaknesses, and codify/absorb more domain knowledge that’s not in the training set. You are talking about context management stuff here, the solution will be something like a proper memory subsystem, maybe some architectural tweaks to integrate it. There are more obvious gaps beyond that which we will have to scaffold and then solve in turn. Another way of thinking about this is just that scaffolding is a much faster way of iterating on solutions than pre-training, or even post-training, and so it will continue to be a valuable way of advancing capabilities.

theptip 11mo ago

The general pattern seems to be that LLM+scaffolding performs better than LLM. In 6 months time a new model will incorporate 80% of your scaffolding, but also will enable new capabilities with a new layer of scaffolding. I suspect the model that doesn’t need scaffolding is simply ASI, as in, the AI can build its own scaffolding (aka recursive self-improvement), and build it better than a human can. Until that point, the job is going to remain figuring out how to eval your frontier task, scaffold the models’ weaknesses, and codify/absorb more domain knowledge that’s not in the training set. You are talking about context management stuff here, the solution will be something like a proper memory subsystem, maybe some architectural tweaks to integrate it. There are more obvious gaps beyond that which we will have to scaffold and then solve in turn. Another way of thinking about this is just that scaffolding is a much faster way of iterating on solutions than pre-training, or even post-training, and so it will continue to be a valuable way of advancing capabilities.