Comment by criemen
5 days ago
> Please provide a citation for wild claims like this. Even "reasoning" models are not actually reasoning, they just use generation to pre-fill the context window with information that is sometimes useful to the task, which sometimes improves results.
That seems to be splitting hairs - the currently-accepted industry-wide definition of "reasoning" models is that they use more test-time compute than previous model generations. Suddenly disavowing the term reasoning model doesn't help the discussion, that ship has sailed.
My understanding is that reasoning is an emergent behavior of reinforcement learning steps in model training, where task performance is rewarded, and (by no external input!) the model output starts to include phrases ala "Wait, let me think". Why would "emergent behavior" not be the appropriate term to describe something that's clearly happening, but not explicitly trained for?
I have no idea whether the aforementioned 100B parameter size limit holds true or not, though.
Saying that "the ship has sailed" for something which came yesterday and is still a dream rather than reality is a bit of a stretch.
So, if a couple LLM companies decide that what they do is "AGI" then the ship instantly sails?
Only matters if they can convince others that what they do is AGI.
As always ignore the man behind the curtain.
Just like esoteric appropriation of 'quantum entanglement', right? It's vibe semantics now.
I'm almost positive reasoning is not an emergent behavior considering the reasoning models have specific architecture. As a source: https://arxiv.org/html/2504.09762v1
> currently-accepted industry-wide definition of "reasoning"
You can't both (1) declare "reasoning" to be something wildly different than what humans mean by reasoning and (2) insist people are wrong when they use the normal definition say models don't reason. You gotta pick a lane.
I don't think its too problematic, its hard to say something is "reasoning" without saying what that something is, for another example of terms that adjust their meaning to context for example, the word "cache" in "processor cache", we know what that is because its in the context of a processor, then there's "cache me outside", which comes from some tv episode.
It's a tough line to tread.
Arguably, a lot of unending discourse about the "abilities" of these models stems from using ill-defined terms like reasoning and intelligence to describe these systems.
On the one hand, I see the point that we really struggle to define intelligence, consciousness etc for humans, so it's hard to categorically claim that these models aren't thinking, reasoning or have some sort of intelligence.
On the other, it's also transparent that a lot of the words are chosen somewhat deliberately to anthropomorphize the capabilities of these systems for pure marketing purposes. So the claimant needs to demonstrate something beyond rebutting with "Well the term is ill-defined, so my claims are valid."
And I'd even argue the marketers have won overall: by refocusing the conversation on intelligence and reasoning, the more important conversation about the factually verifiable capabilities of the system gets lost in a cycle of circular debate over semantics.
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Or you could accept that sometimes fields contain terms-of-art that are non-intuitive to outsiders. Go ask an astromer what their working definition of a metal is.
No. This is the equivalent of an astronomer telling a blacksmith they're using the term "metal" incorrectly. Your jargon does not override everyone else's language.