I can't wait until the RSI fad dies down for awhile and we can get a break from all the intellectual grandstanding...
The code they released doesn't match what's in the paper.
And the results they got are significantly narrower than the narrative heft being asked for by their word choice ("To advance beyond humanity’s accumulated knowledge", "AI’s Odyssean moment").
I'm tired boss.
> RSI fad dies down
Don't fall for the "marketing hype" antihype. RSI is a trendy term today, and lots of people discuss it, but it's nothing new. We've basically had forms of RSI since the start of the LLM craze. Previous models have been used to improve next versions for ~3 years now. Just because humans used to guide / select / decide when and how to apply those models, doesn't make it less recursive or self or improvement. We're now seeing what happens when we remove the humans from some of the tasks, but the basics are still there. Use prev model to make next model better.
This is not a fad - this is a consequence of the Bitter Lesson. RSI is the only game in town now.
I don't get it. TBL is an observation, not a law or guarantee. It says (Compute / $) is increasing. In efficiency terms:
useful_work / $ = (useful_work / compute) × (compute / $)
But the left side only improves if the method can turn that extra computation into useful output.
RSI is about turning that extra computation into acceleration - that is applying the compute to the (useful_work / compute) factor - this is the recursive part.
plausible, but unclear
It says much more than that. It says that all custom architectures and approaches are become irrelevant if compute increases, which was proven again by the large latent models recently solving RL tasks better than any classical RL technique.
RSI is also known under more mundane words such as "Learning".
If they had something of value, as a research neolab, they would not just release it but build it into their systems and use it.
Most of these agentic swarm systems are memory and context managers/compressors. However it's clear from both the paper and the blog that everything is just natural-language-based and generated, effectively making it slop from start to finish.
Instead of describing how or what was stored by each agent, and how, in what format, how it was selected, the whole process is described as Claude-generated workflows and pipelines, which brings 0 value.