I know we have strong views on what a truly open model is (open weights, open training data, open training code etc.) but I really like how transparent they’ve been about the training of this model.
The realtime dashboard they shared during training (<a href="https://mimo.xiaomi.com/rl/" rel="nofollow">https://mimo.xiaomi.com/rl/) was an incredible learning and teaching tool for me, and they’ve been unusually comprehensive in sharing details about their methodology (check out that tech report - it's got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores (even the stuff they didn’t do well on).
If you’re releasing an open model going forward, please consider offering the community more of this transparency!
maybe this is why Dario want to slow down AI development and all the big AI labs in the USA is singing the same song.
whey they all singing the same tune. it make me question what is their real motives.
they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.
Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.
The general idea is that Anthropic/OpenAI is pushing this narrative as an attempt at "Regulatory Capture"[1] which would allow them to make it prohibitively expensive for anyone but them to enter the market thus stifling competition.
I heard someone analogize token vendors to car manufacturers, where American companies only want to produce expensive options, the people want cheaper/better alternatives, and we ban BYD because those with enough money are more "persuasive"
The analogy is a good one, but your explanation is missing one aspect: the country (USA) does have a reasonable interest in having the capacity to build their own models. The “we need to slow down because it’s getting too dangerous” part is probably more related to “we need to slow our public facing development down so the US government can get the best and the American corporations can trickle out what we decide is safe”
It’s similar with cars. It’s not that American cars are better than Chinese cars on any tangible measurement. But America already shipped most of its manufacturing overseas. Everyone who built those factories is retired. The US should probably hold on to some capacity to make cars, seeing as their entire infrastructure depends on them.
American Ai/Car manufacturers could build cheaper/open models, some do, the big ones do not. It's not an either or, but a spectrum where they have chosen to build only in a subrange
That doesn’t explain the decline of German automotive industry which is now taken over by Chinese cars thanks to massive subsidies by the Chinese government
It's a matter of scale: <a href="https://www.wsj.com/world/china/the-u-s-has-been-spending-billions-to-revive-manufacturing-but-china-is-in-another-league-75ed6309" rel="nofollow">https://www.wsj.com/world/china/the-u-s-has-been-spending-bi...
If you trust Google's AI summary, China spends 4-5% of GDP on industrial subsidies, vs US at 0.4%. 10-12x as much.
Another point of comparison we might make, Trump's desired increase to the US Defense budget is how close to what China is spending on industrial subsidies? It looks relatively close to numbers in these research papers.
It’s not a reasonable comparison. In China, every corporation is de facto state run. The Party is in the boardroom and the executives are members of the Party. The CCP will build entire mega cities or pump money into this industry or that according to their plan.
rao-v · · focus · HN ↗
The realtime dashboard they shared during training (<a href="https://mimo.xiaomi.com/rl/" rel="nofollow">https://mimo.xiaomi.com/rl/) was an incredible learning and teaching tool for me, and they’ve been unusually comprehensive in sharing details about their methodology (check out that tech report - it's got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores (even the stuff they didn’t do well on).
If you’re releasing an open model going forward, please consider offering the community more of this transparency!
MangoCoffee · · focus · HN ↗
whey they all singing the same tune. it make me question what is their real motives.
they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.
jwolfe · · focus · HN ↗
rbjorklin · · focus · HN ↗
* 1: <a href="https://en.wikipedia.org/wiki/Regulatory_capture" rel="nofollow">https://en.wikipedia.org/wiki/Regulatory_capture
verdverm · · focus · HN ↗
juiceland · · focus · HN ↗
It’s similar with cars. It’s not that American cars are better than Chinese cars on any tangible measurement. But America already shipped most of its manufacturing overseas. Everyone who built those factories is retired. The US should probably hold on to some capacity to make cars, seeing as their entire infrastructure depends on them.
verdverm · · focus · HN ↗
juiceland · · focus · HN ↗
nxm · · focus · HN ↗
verdverm · · focus · HN ↗
In other words, when do economic and industrial policies transition to subsidies? Is it a matter of perspective?
creato · · focus · HN ↗
If you trust Google's AI summary, China spends 4-5% of GDP on industrial subsidies, vs US at 0.4%. 10-12x as much.
verdverm · · focus · HN ↗
verdverm · · focus · HN ↗
<a href="https://www.csis.org/analysis/red-ink-estimating-chinese-industrial-policy-spending-comparative-perspective" rel="nofollow">https://www.csis.org/analysis/red-ink-estimating-chinese-ind...
Some historical analyses of US policies (know less, but both put it over 1% currently, nuances)
<a href="https://www.columbia.edu/~ev2124/research/ErtenStiglitzVerhoogen&Wilse-SamsonJune2026.pdf" rel="nofollow">https://www.columbia.edu/~ev2124/research/ErtenStiglitzVerho...
<a href="https://www.nber.org/system/files/working_papers/w34744/w34744.pdf" rel="nofollow">https://www.nber.org/system/files/working_papers/w34744/w347...
<a href="https://www.columbia.edu/~ev2124/research/ErtenStiglitzVerhoogen&Wilse-SamsonJune2026.pdf" rel="nofollow">https://www.columbia.edu/~ev2124/research/ErtenStiglitzVerho...
verdverm · · focus · HN ↗
juiceland · · focus · HN ↗