this post was submitted on 08 Jun 2025
769 points (95.6% liked)
Technology
71143 readers
4088 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
That indicates that this particular model does not follow instructions, not that it is architecturally fundamentally incapable.
Not "This particular model". Frontier LRMs s OpenAI’s o1/o3,DeepSeek-R, Claude 3.7 Sonnet Thinking, and Gemini Thinking.
The paper shows that Large Reasoning Models as defined today cannot interpret instructions. Their architecture does not allow it.
those particular models. It does not prove the architecture doesn't allow it at all. It's still possible that this is solvable with a different training technique, and none of those are using the right one. that's what they need to prove wrong.
this proves the issue is widespread, not fundamental.
Is "model" not defined as architecture+weights? Those models certainly don't share the same architecture. I might just be confused about your point though
It is, but this did not prove all architectures cannot reason, nor did it prove that all sets of weights cannot reason.
essentially they did not prove the issue is fundamental. And they have a pretty similar architecture, they're all transformers trained in a similar way. I would not say they have different architectures.
Ah, gotcha
The architecture of these LRMs may make monkeys fly out of my butt. It hasn't been proven that the architecture doesn't allow it.
You are asking to prove a negative. The onus is to show that the architecture can reason. Not to prove that it can't.
that's very true, I'm just saying this paper did not eliminate the possibility and is thus not as significant as it sounds. If they had accomplished that, the bubble would collapse, this will not meaningfully change anything, however.
also, it's not as unreasonable as that because these are automatically assembled bundles of simulated neurons.