Reinforcement Learning with Verifiable Rewards (RLVR) to improve math and coding success rates seems like an exception.
If someone was already evaluating the work output using a metric closer to the underlying quality then it might not have been a big shift for them (other than having much more work to evaluate).
You could however only do that if you were fine with unfairly judging the quality of work, as you now readily discarded quality work based on superficial proxies. Which admittedly is done in a lot of cases.
I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level.
And AI signatures are now easy for people to recognize. In fact, these turns of phrase aren't just recognizable—they're unmistakable. <-- See what I did there?
Having worked with corporate clients for 10 years, I don't view the pre-LLM era as a golden age of high-quality knowledge work. There was a lot of junk that I would also classify as a "working simulacrum of knowledge work."
This is especially true if we start to see more of a split in usage between LLMs based on cost. High quality frontier models might produce better work at a higher cost, but there is also economic cost pressure from the bottom. And just like with human consultants or employees, you’ll pay more for higher quality work.
I’m not quite sure what I’m trying to argue here. But the idea that an LLM won’t produce a low quality report just seemed silly to me.
I can see a similar problem with this article - the author notices that LLMs produce a lot of errors - then concludes that they are useless and produce only simulacrum of work. The author has an interesting observation about how llms disrupt the way we judge knowledge work. But when he concludes that llms do only simulacrum of work - this is where his arguments fail.
Wait, you're probably talking about the test of discarding a report based on something superficial like spelling errors. Which fails with LLMs due to their basic conman personalities and smooth talking. And therefore ..?
For most tasks, the complexity/time required to verify a task is << the time required to do the task itself. Sure there can be hallucinations on the graph that the LLM made. But LLMs are hallucinating much less than before. And the time to verify is much lower than the time required for a human to do the task.
I wrote a post detailing this argument https://simianwords.bearblog.dev/the-generation-vs-verificat...
`simulacrum` is a great word, gotta add that to my vocabulary.
balamatom•1h ago
Yes.
This does not however mean that progress is not being made.
It just means the progress is happening along such dimensions that are completely illegible in terms of the culture of the early XXI century Internet, which is to say in terms of the values of the society which produced it.
downboots•1h ago
balamatom•1h ago