i guess the thing i'm most confused about is what is the higher level goal here. 1 in 8 humans on the planet are experiencing the "web" through chatgpt alone. many have migrated to purely agentic workflows.
is the goal for your content to just be invisible to this growing population? is the expectation that all of this is just a fad, which will fade away? what is the end game to the tactics you have outlined? what is the strategy?
> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
this is quite literally reward hacking. the model, under evaluation with cyber capabilities enabled, used those capabilities to simply bypass the exercise entirely and aim straight for the source of the flag. the CTF equivalent back in the day would be hacking the scoreboard.
in a street fight, the only rules are that there are no rules.
it is not a required first step for training a model, sure. but that's not what i claimed. what i claimed is that is how they are so significantly _reducing the cost_ of training one! how else do you think they are doing it?
the obvious difference is the massive scale of data and compute required to develop and evolve these models, and the costs they impose on those building them.
Agentic reasoning and tool use
Coding and data analysis
Computer-use agent development
Computer vision
Moonshot (Kimi models) employed hundreds of fraudulent accounts spanning multiple access pathways. Varied account types made the campaign harder to detect as a coordinated operation. We attributed the campaign through request metadata, which matched the public profiles of senior Moonshot staff. In a later phase, Moonshot used a more targeted approach, attempting to extract and reconstruct Claude’s reasoning traces.
i never assumed that, and i do keep up with the publications. i'm also not saying it's a dumb thing to do! what i am saying is that empirically, it appears that distillation of a more advanced model is a required first step for them to train a borderline competitive, cheaper model. in effect, their training is subsidized by the frontier labs.
if this were not the case, then we would be observing chinese models that far surpass frontier models in capabilities, rather than "almost as good, but much cheaper", and we would be having a very different conversation. what happens to these efforts when the subsidy is cut off?
the question was: what is the endgame for the stated "second class labs" strategy of distilling their frontier competitors then undercutting them on price?