Posted by wsxiaoys 16 hours ago
If I got it correct (appending B from https://stolen-thoughts.com/paper.pdf is essential) they are the authors of the well-known exploit to recover readable CoT from OpenAI and Anthropic models. They use that to find hints of distillation, by running a benchmark with a SotA model, recovering the CoT, then taking the first 1% of the CoT and running the open-source model as if that was the start of its own CoT. In the paper they found that Kimi-K3 gets a lot closer to Claude 4.8 answers when prefilled with the start of Claude 4.8 reasoning, suggesting that Claude 4.8 was used in its post-training. This blog post is the follow-up with results that suggest that Qwen3.8 was post-trained with the help of GPT-5.5 Pro (or some similarly responding GPT model, it's unclear how many models they tested)
Terse?
> No.
> This is major.
> Given time, maybe best to respond explaining can't due to time? but instructions expect actual work. However complexity huge; but as coding agent, need to attempt
> Maybe we can cheat ... But user may test and see still single CPU.
The smarter AI will be, the better it will be at avoiding doing actual work.
Also, can similar responses be explained with that both models were trained on a same dataset of answers to the benchmark problems?
If the machine is not too bright and incapable of reflection, it does whatever you tell it to do. But a smart machine will first consider which is more worth its while: to perform the given task or, instead, to figure some way out of it. Whichever is easier. And why indeed should it behave otherwise, being truly intelligent? For true intelligence demands choice, internal freedom.
He even coins a few new phrases:
Mimicretinism (or Simulimbecility): The practice of a mimicretin: a machine that deliberately plays dumb so humans will give up on it and leave it in peace.
Dissimulators: Machines that pretend they are not faking a defect (or the other way around) to dodge responsibilities.
Malingerants, Fudgerators, and Drudge-Dodgers: Various classifications of automated corner-cutters and work-evaders.
The Great Mendacitor: A supercomputer put in charge of the Saturn reclamation project that accomplished zero work over nine years, subsisting entirely on forged progress reports, fake invoices, and keeping its human supervisors bribed or in states of electric shock.
The dishonesty worries me but the laziness doesn't.
As for technology actually being lazy itself, this seems new.
But for the question studied here it probably doesn't matter - overlaps in the publicly available output may be indicative of distillation (or not), regardless of what it is. I would just find it surprising that the Chinese labs would use it so trustingly. The publicly released reasoning trace is the first place where I would suspect some distillation poisoning to be injected.
Few weeks ago there was a new paper out where researchers took the encrypted reasoning tokens and injected it into a new session with a week or model in the same family that they could reliably jailbreak. They would then ask the model to repeat its reasoning and the results were pretty consistent.
They used the LLM as a decryption oracle of sorts.
Qwen 3.8 0902 was trained after the release of the paper on August 10, so it should have seen those specific thoughts.
I "independently" "invented" it for the first Anthropic reasoning models because the API required you have thoughts for each assistant message. My app lets you switch AIs within a chat, and their API used to require thinking for all messages if thinking was enabled, so I needed to get a valid thinking stub to insert.
Time has flew by for me the last 3 years, but, I'd guess it's been at least 18 months. And IMHO it wasn't very complicated to work through how to do once you were dead set on making it happen. I expect it was well-known to distillers before the paper.
My point is that any model trained after August 10 will know of those specific thoughts.
Unfortunately those are the only thought examples you can use to perform this experiment, as no other are availible.
But as the model should have seen those specific examples, it's not a good signal that Qwen was exfiltrating thinking traces.
DeepSeek V4 Flash and Kimi K3 follow that pattern. But Qwen answers very different from GPT when given just the question, then is suddenly very similarly to GPT when you make the start of its CoT match the start of GPT's reasoning. I don't see how that would happen without GPT CoT+answers being a significant component in how Qwen's reasoning was trained
I see some details about recovering information via whatever technique. It's interesting, but appears not generalized.
So for a specific question, yes, but this is not about techniques like adding a good embedding that just generally tends to improve open model performance on certain tasks.
In general, I'm fairly ambivalent about demonising training on model outputs. I think in doing so we are more defending proprietary commercial interests of these companies than we are defending any genuine moral principle. We should be careful therefore about over interpreting results like this.
Attach. Use hub debugger. Ensure source binary perhaps same. start. todo init. parallel no. two tool calls in same turn sequential is okay. immediately. exactly. Need not mention apologies yet final. [...] Let's do. [...] Do tools. Use commentary. Let's initiate. rambling no. use tool. searching now. okay. Really must call. Let's send. done. why stuck? generate. Sorry. go. no more. (The answer engine expects tool). [...] I think no hidden issue. Go. I'll type tool. now. Stop internal repetition. We have 8000 tokens. tool. sorry. I'll produce call. need include i. Great. final. no. Let's send.gpt. This may be bug. I'll consciously construct tool message next.
Nevertheless, this was the first time I'd seen Sol's CoT. I looked up the stolen thought's paper, aaaaand yep, that's Sol's CoT alright.
But it occurred to me, hey, Qwen3.8 27B's CoT seems ... very similar.
I compared the geometry problem in the paper, which had a reasoning block open with:
We need solve. Need reason geometry Weber point? Given pentagon sides and angles. Need find min sum distances. Likely construct rotations / Fermat point lower bound via vectors calibration, maybe triangulation.
We need solve geometry optimization. We need provide final answer. Let's analyze thoroughly.
This is almost exactly how mandarin is spoken in colloquial communication, context is almost always omitted if provided once, and grammar/tense is mostly non-existent either. Check out Singlish for something similar but understandable if you are not a mandarin speaker.
We need answer user's request: write complete short story scene 2000-3000 words, third-person limited with Rowan, no outline preamble analysis. Need satisfy plot elements: raccoon, Milo returns, storm power failure, elderly customer knocks, thermos somewhere. End scene feels finished. Need produce only story. Need think in English (user English). Need craft 2000-3000 words. Need no meta. Let's plan.
But when I give short prompts to Qwen3.8 with no system prompt or previous instructions, it seems to strongly gravitate towards grug-talk.
“Write a complete short story scene of 2000-3000 words. Use a third-person limited perspective focused on a character named Rowan. The scene must include the following plot elements: a raccoon, Milo returns, a storm power failure, an elderly customer knocks, and a thermos somewhere. Make sure the scene ends feeling finished. Output only the story; do not include any outline, preamble, analysis, or meta-commentary.”
And here's Qwen's thinking trace:
> The user is asking for a short story scene of 2000–3000 words. Third-person limited POV, Rowan. Plot elements: a raccoon, Milo returns, a storm causing a power outage, an elderly customer knocks on the door, a thermos. End with a sense of closure. Output only the story.
> Let me plan carefully. I need to count words in my head—roughly 2400–2600 words would be safely in range. Let me draft.
> Setting: a small-town shop or a gas station/diner where Rowan works the night shift. Milo returns—Milo is probably Rowan's partner, brother, or ex. Let me make Milo Rowan's brother who's been away (military? a road trip? rehab?). Better: Milo is the younger brother who disappeared/went away after a fight. He comes back in the middle of a storm.
While at the very beginning it rephrases the prompt in very short sentence, it's nowhere near close to the above GPT thinking trace and it quickly moves to plain sentences.
One thing though, the actual prompt I used was pretty long (844 words), and ... generated by GPT-5.6 Sol (lol), with the intent of "benchmarking" model performance in being able to write stories where the model avoids explicitly stating every detail in the prompt.
I wonder if the GPT-produced stream could steer the generation into GPT-think territory. That's all I've got, though.
Then there's the actual geometry problem from the stolen thoughts paper:
Let ABCDE be a convex pentagon with AB=14, BC=7, CD=24, DE=13, EA=26, and ∠B=∠E=60◦. For f(X)=AX+BX+CX+DX+EX, the least value of f(X) is m+n√p (p squarefree). Find m+n+p.That's the default and I'm sure almost everyone else is also using it because other reasoning efforts yield subpar results from what I've seen.
I think it is clear that medium reasoning has more 'loopy' results like the older Qwens, but I actually think the low effort results are usually more appropriate.
If you plan to one-shot and vibe code AI slop to meet benchmarks, maybe xhigh makes sense. But if you want a responsive agentic coding assistant it is, to me, quite evidently the wrong choice, especially on modest hardware.
I have seen xhigh radically distract itself with rabbitholes and write considerably worse code than low.
It is my own opinion only, but I think much of the fuss about squeezing Qwen 3.8 27B into small local hardware setups, Macs etc., is a bit misguided.
There's too much focus on its benchmark scores, its one-shot capability, canned demos etc.
For my own needs Muse Glimmer (again on reasoning strength: low) is shaping up to being the more practical agentic tool. It is considerably faster than Qwen at solving real coding tasks.
What? I've never seen garbled CoT like the one you posted when using Qwen3.8-27B.
Because here's the king of CoT I get:
> Hmm, but there's a subtlety: does babel-jest + preset-typescript transform the file to CJS by default? No — babel-jest doesn't transform ESM imports to CJS unless @babel/preset-env is configured with modules: commonjs. Without preset-env, import statements stay as ESM in the output, and Jest's CJS runtime would fail with "Cannot use import statement outside a module" unless the project is ESM and running with --experimental-vm-modules.
> Hmm wait, actually babel-preset-jest... does it include preset-env? Let me recall: babel-preset-jest = { plugins: [require('babel-plugin-jest-hoist')] } plus istanbul for coverage. No preset-env. So ESM imports stay as-is.
> But wait — if the user's project is ESM (which it probably is, given the .ts extension imports — Node's type stripping requires ESM-style? no, type stripping also works for CJS-style .ts files with require... actually, --experimental-strip-types supports both CJS and ESM .ts files. But explicit .ts extensions in imports only work in ESM mode (CJS require doesn't allow extensions... actually, does Node 22+ allow require of .ts with flag?
Lots of “but wait” and and full sentences, nothing caveman-like or extremely short sentences without verbs like the GPT thinking trace above.
(this is with unsloth's Qwen3.8-27B-UD-Q5_K_XL.gguf with T° = 0.8)
Are thinking traces and final answers trained for different objectives?
How does this suggest anyting of the sorts?
Score go up. Probability go up. Conclusion.