One clarification: this is not a zero-sum game between miners (prompters) and validators (judges). The validators will reward outputs adhering to whatever criteria the protocol designates as “valuable”. The adversarial term mostly refers to miners vs. [misaligned] LLMs.
So this is why I’m here:
What are the “wish list” schema dimensions (miners and validators both) from a research perspective?
What is the best way to aggregate, tag/label, categorize and package this into datasets that researchers can draw meaningful conclusions from?
What is an intelligent way to define and iterate “valuable” outputs (the mechanism itself will be python based script) from miners on the protocol, so that Aurelius becomes an engine for standardized alignment data.
I’m have some ideas of my own, but I’m here to hear it straight from the source.
So I ask anyone here, If you had access to a vast network of independent compute, all with their own prompting and scoring strategies, what would you ideally like to learn from that?
Yes you’ve got the idea framing exactly right.
One clarification: this is not a zero-sum game between miners (prompters) and validators (judges). The validators will reward outputs adhering to whatever criteria the protocol designates as “valuable”. The adversarial term mostly refers to miners vs. [misaligned] LLMs.
So this is why I’m here:
What are the “wish list” schema dimensions (miners and validators both) from a research perspective?
What is the best way to aggregate, tag/label, categorize and package this into datasets that researchers can draw meaningful conclusions from?
What is an intelligent way to define and iterate “valuable” outputs (the mechanism itself will be python based script) from miners on the protocol, so that Aurelius becomes an engine for standardized alignment data.
I’m have some ideas of my own, but I’m here to hear it straight from the source.
So I ask anyone here, If you had access to a vast network of independent compute, all with their own prompting and scoring strategies, what would you ideally like to learn from that?