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Qwen3: Think deeper, act faster

qwenlm.github.io

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Re: Qwen3: Think deeper, act faster

#231

Post by oscord »

stavros wrote: post #134
concrete_head wrote: post #131

Earlier quoted context omitted.

Can you please share the problem?

I don't really want it added to the training set, but eh. Here you go: > Assume I have a 3D printer that's currently printing, and I pause the print. What expends more energy, keeping the hotend at some temperature above room temperature and heating it up the rest of the way when I want to use it, or turning it completely off and then heat it all the way when I need it? Is there an amount of time beyond which the ans…

Qwen3-32b did it pretty accurately it seems. Calculated heat loss over time going to ambient temp, offered to keep it at standby 100C for short breaks under 10 minutes. Shut down completely for longer breaks.

Re: Qwen3: Think deeper, act faster

#233

Post by jingyibo123 »

johanyc wrote: post #142
eunos wrote: post #42

Earlier quoted context omitted.

The avoiding talking part is more on the Frontend level censorship I think. It doesn't censor on API

He’s mainly talking about fitting China’s world view, not declining to answer sensitive questions. Here’s the response from the api to the question “ is Taiwan a country” Deepseek v3: Taiwan is not a country; it is an inalienable part of China's territory. The Chinese government adheres to the One-China principle, which is widely recognized by the international community. (omitted) Chatgpt: The answer depends on how…

I guess both is "factual", but both is "biased", or 'selective'.

The first part of ChatGPT's answer is correct: > The answer depends on how you define “country” — politically, legally, and practically

But ChatGPT only answers the "practical" part. While Deepseek only answers the "political" part.

Re: Qwen3: Think deeper, act faster

#234

Post by novaRom »

SamBam wrote: post #165
furyofantares wrote: post #145

Earlier quoted context omitted.

I similarly have a small, simple spatial reasoning problem that only reasoning models get right, and not all of them, and which Qwen3 on max reasoning still gets wrong. > I put a coin in a cup and slam it upside-down on a glass table. I can't see the coin because the cup is over it. I slide a mirror under the table and see heads. What will I see if I take the cup (and the mirror) away?

Yup, it flunked that one. I also have a question that LLMs always got wrong until ChatGPT o3, and even then it has a hard time (I just tried it again and it needed to run code to work it out). Qwen3 failed, and every time I asked it to look again at its solution it would notice the error and try to solve it again, failing again: > A man wants to cross a river, and he has a cabbage, a goat, a wolf and a lion. If he le…

4o with thinking:

By systematic (BFS) search of the entire 32-state space under these rules, one finds no path from to that stays always safe. Thus the puzzle has no solution—there is no way for the man to ferry all four items across without at least one of them being eaten.

Re: Qwen3: Think deeper, act faster

#235

Post by Alifatisk »

janalsncm wrote: post #147
WhitneyLand wrote: post #125

China is doing a great job raising doubt about any lead the major US labs may still have. This is solid progress across the board. The new battlefront may be to take reasoning to the level of abstraction and creativity to handle math problems without a numerical answer (for ex: https://arxiv.org/pdf/2503.21934 ). I suspect that kind of ability will generalize well to other areas and be a significant step toward human…

No kidding. I’ve been playing around with Hunyuan 2.5 that just came out and it’s kind of amazing.

Where do you play with it? What shocks you about it? Anything particular?

Re: Qwen3: Think deeper, act faster

#236

Post by Me1000 »

pxc wrote: post #155
johanyc wrote: post #142

Earlier quoted context omitted.

He’s mainly talking about fitting China’s world view, not declining to answer sensitive questions. Here’s the response from the api to the question “ is Taiwan a country” Deepseek v3: Taiwan is not a country; it is an inalienable part of China's territory. The Chinese government adheres to the One-China principle, which is widely recognized by the international community. (omitted) Chatgpt: The answer depends on how…

When I tried to reproduce this, DeepSeek refused to answer the question.

There’s an important distinction between the open weight model itself and the deepseek app. The hosted model has a filter, the open weight does not.

Re: Qwen3: Think deeper, act faster

#238

Post by OtherShrezzing »

simonw wrote: post #38

Something that interests me about the Qwen and DeepSeek models is that they have presumably been trained to fit the worldview enforced by the CCP, for things like avoiding talking about Tiananmen Square - but we've had access to a range of Qwen/DeepSeek models for well over a year at this point and to my knowledge this assumed bias hasn't actually resulted in any documented problems from people using the models. Asid…

>Has this turned out to be less of an issue for practical applications than was initially expected? Are the models just not censored in the way that we might expect?

I think it's the case that only a handful of very loud commentators were thinking about this problem, and they were given a much broader platform to discuss it than was reasonable. A problem baked into the discussion around AI, safety, censorship, and alignment, is that it's dominated by a fairly small number of close friends who all loudly share the same approximate set of opinions.

Re: Qwen3: Think deeper, act faster

#239

Post by SubiculumCode »

EMIRELADERO wrote: post #36
margorczynski wrote: post #19

Any news on some viable successor of LLMs that could take us to AGI? As I see they still can't solve some fundamental stuff to make it really work in any scenario (halucinations, reasoning, grounding in reality, updating long-term memory, etc.)

A mixture of many architectures. LLMs will probably play a part. As for other possible technologies, I'm most excited about clone-structured causal graphs[1]. What's very special about them is that they are apparently a 1:1 algorithmic match to what happens in the hippocampus during learning[2], to my knowledge this is the first time an actual end-to-end algorithm has been replicated from the brain in fields other th…

[1] seems to be an amazing paper, bridging past relational models, pattern separation/completion, etc. As someone who's phd dealt with hippocampal dependent memory binding, I've always enjoyed the hippocampal modeling as one of the more advanced areas of the field. Thanks!

Re: Qwen3: Think deeper, act faster

#240

Post by paradite »

coder543 wrote: post #175
croemer wrote: post #118

The benchmark results are so incredibly good they are hard to believe. A 30B model that's competitive with Gemini 2.5 Pro and way better than Gemma 27B? Update: I tested "ollama run qwen3:30b" (the MoE) locally and while it thought much it wasn't that smart. After 3 follow up questions it ended up in an infinite loop. I just tried again, and it ended up in an infinite loop immediately, just a single prompt, no follow…

Another thing you’re running into is the context window. Ollama sets a low context window by default, like 4096 tokens IIRC. The reasoning process can easily take more than that, at which point it is forgetting most of its reasoning and any prior messages, and it can get stuck in loops. The solution is to raise the context window to something reasonable, such as 32k. Instead of this very high latency remote debugging…

I can't belive Ollama haven't fix the context window limits yet.

I wrote a step-by-step guide on how to setup Ollama with larger context length a while ago: https://prompt.16x.engineer/guide/ollama

TLDR

  ollama run deepseek-r1:14b
  /set parameter num_ctx 8192
  /save deepseek-r1:14b-8k
  ollama serve
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