Each time you define another task well enough for the system to work, you generalize the system just a little bit - repeat enough times and you can start to expand, develop taxonomies of functions, precisely define function spaces and metrics for improvement. This might not be a bootstrap for recursive self improvement generally, but it could definitely inform the theory or design of a system that does bootstrap rsi.
The structure of their research - the process, the specific task, and the data they generate - will help inform how other research gets performed. Instead of GPU kernels, maybe the next task is something like neuron modules, looking for structures that improve on attention blocks, or things like that - each time you run through an experiment like this, you're creating foundational data upon which other experiments can be run and improved. Once you've done enough of them, you can generalize.
It could be that the end result is the knowledge of strict boundaries of LLM capabilities, that they can only operate in specific domains, or only improve to a certain extent, and some currently unspecified defect limits the level of improvement.
The underlying idea of specifying a domain and task conditions, then letting an LLM run thousands of experiments, is a great search technique. The hope is that there is no implicit defect and that the methodology will extend and generalize - it's not too complex a notion to think that you could have an LLM create a broad range of individual tasks, with a meta-goal of identifying better and more general recursive improvement processes and algorithms.
>The hope is that there is no implicit defect and that the methodology will extend and generalize - it's not too complex a notion to think that you could have an LLM create a broad range of individual tasks, with a meta-goal of identifying better and more general recursive improvement processes and algorithms
Again, entirely different idea that doesn't have a straightforward evaluation function. As it stands, this is more akin to genetic programming with a very good mutation function.
