Anthropic has a nice press release that summarises it in simpler terms: https://www.anthropic.com/research/reasoning-models-dont-say...

Ask an LLM!

I don't either, but chain of thought is obviously bullshit and just more LLM hallucination.

LLMs will routinely "reason" through a solution and then proceed to give out a final answer that is completely unrelated to the preceding "reasoning".

The thing with alchemy was not that their hypotheses were wrong (they eventually created chemistry), but that their method of secret esoteric mysticism over open inquiry was wrong.

Newton is the great example of this: he led a dual life, where in one he did science openly to a community to scrutinize, in the other he did secret alchemy in search of the philosopher's stone. History has empirically shown us which of his lives actually led to the discovery and accumulation of knowledge, and which did not.

I'm not sure I'd throw out all the alignment baby with the bathwater. But I wish we could draw a distinction between "Might offend someone" with "dangerous."

Even 'plotting terror attacks' is not something terrorists can do just fine without AI. And as for making sure the model wouldn't say ideas that are hurtful to <insert group>, it seems to me so silly when it's text we're talking about. If I want to say "<insert group> are lazy and stupid," I can type that myself (and it's even protected speech in some countries still!) How does preventing Claude from espousing that dumb opinion, keep <insert group> safe from anything?

That's probably true... right up until it reports you to the police.

Oh sure, RLHF instruction tuning was what turned an model of mostly academic interest into a global phenomenon.

But it also compromised model accuracy & performance at the same time: The more you tune to eliminate or reinforce specific behaviours, the more you affect the overall performance of the model.

Hence my speculation that Anthropic is using a chain-of-thought model that has not been alignment tuned to improve performance. This would then explain why you don’t get to see its output without signing up to special agreements. Those agreements presumably explain all this to counter-parties that Anthropic trusts will cope with non-aligned outputs in the chain-of-thought.

Ugh, I'm past the edit window, but I meant RLHF aka "Reinforced Learning from Human Feedback", I'm not sure how I messed that up not once but twice!

I think qwen3:R1 is apples:oranges, if you mean the 32B models. R1 has 20x the parameters and likely roughly as much knowledge about the world. One is a really good general model, while you can run the other one on commodity hardware. Subjectively, R1 is way better at coding, and Qwen3 is really good only at benchmarks - take a look at aider‘s leaderboard, it’s not even close: https://aider.chat/docs/leaderboards/

R2 could turn out really really good, but we‘ll see.

They mopped the floor in terms of transparency, even more so in terms of performance × transparency

Long term that might matter more

DeepSeek made OpenAI panic, they initially hid the CoT for o1 and then rushed to release o3 instead of waiting for GPT-5.

I disagree. I find myself constantly going to their free offering which was able to solve lots of coding tasks that 3.7 could not.

counterpoint: influencers said they wiped the floor with everyone so it must have happened

I had assumed it was a way to reduce "hallucinations". Instead of me having to double check every response and prompt it again to clear up the obvious mistakes it just does that in the background with itself for a bit.

Obviously the user still has to double check the response, but less often.