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)
they should call themselves real-time archaelogists: They dig up the past cause it's interest, but mostly meaningless and done by people with way too much funding for what they provide the rest of us with understanding.
I wasn't aware that we have access to raw reasoning tokens? I thought what you get is a kind of summary. Does the author have some kind of privileged access or was my assumption wrong?
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.
They reference this paper which describes a method to decrypt reasoning traces (by sending the encrypted trace back to the model and asking it to transcribe it):
Reasoning works as long as there is a consistent latent space representation. Any kind of poison will just become part of the representation. There's evidence that even directly training on encrypted reasoning traces works, because the length is already a strong signal.
The thoughts trick was known before their paper / August.
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.
I'm sorry, it's going over my head still - my reading is "all models with any training after August 10 know how GPT 5.5 Pro thinks", but I'm not sure why - my initial guess was that's when GPT 5.5 was released, but that doesn't seem to be the case (it was released April 23rd).
I'm not sure how viable it still is. Perhaps it's still possible, and perhas that's exacly what they did in wtich case my objection falls, but I don't know.
As a user of local models, does this mean that there are 'magic incantations' that can increase the performance of some local models?
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.
I don't think that follows from the published results. Would have been an interesting hypothesis to add though, and quite easy. Just throw the same setup at some benchmarks.
It's interesting that someone else noticed this. A week or two ago, GPT-5.6 Sol starting leaking reasoning into a tool call in Pi. I don't really know what happened, but it was ... interesting:
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.
It eventually triggered some error state and stopped.
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.
I passed the same prompt to Qwen, which opened with:
We need solve geometry optimization. We need provide final answer. Let's analyze thoroughly.
This proves nothing, but it does seem an awful lot like they did use GTP-5.5/6 reasoning traces...
This is Unsloth's UD-Q4_K_S quantization (edit -- on llama.cpp, via the Vulkan backend, on an RX 7900 XT, with Unsloth's recommended sampler config), for "as replicable as LLMs can be" disclosure, done through llama.cpp's web UI. In another thread, via Open WebUI (both with no custom prompt, so whatever the "default" system prompt is), a reasoning block opens as such:
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.
Now, I will add that when I use Qwen3.8 in a coding harness, the reasoning tends to NOT look like this, and instead looks more Qwen3.6-ish.
But when I give short prompts to Qwen3.8 with no system prompt or previous instructions, it seems to strongly gravitate towards grug-talk.
I just tried to reproduce in Llama-cpp's webui: with the following (reverse-engineered) prompt:
“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.
Yeah, I dunno. For me it does "talk normally" for the most part when used in an actual coding harness.
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.
The person evaluating and noticing similar reasoning traces to gpt is because they are using a coding harness which probably has a different system prompt to llama webui which primarly serves as a chat interface
It's been pretty obvious to me that the Chinese labs are operating mostly on a fast-follow strategy. The distillation attacks are well-documented, and there is good reason to believe they are able to copy architectural innovations as well. If US labs stagnate I would expect Chinese labs to stagnate as well. Their engineering is great, but in terms of frontier innovation (which requires heavy compute to search for new strategies that work at frontier scale) they are very far behind.
The way I see it the Chinese labs are optimizing for other things, including effective compact models that don't need to run on top-of-the-line nVidia hardware.
It makes me happy because it means that these misanthropic technofascists have no moat. They can spend trillions of dollars only for it to be largely copied in short order.
Even if they weren't political adversaries of freedom, I would still feel 0% bad given all their training is already on data they got for free.
Information continues to want to be free. To the benefit of us all.
Preach brother, they stole everything on the internet, and beyond, to train their models. They thought all that information was free, and everyone a few months beyond them is just following their example.
They didn't actually steal in the sense that the information is still there on the internet.... About these shredded rare books, now we're talking.
If I may propose instead of "steal" I think we could agree to write they "Aaron-Swartz'ed" the information from the internet, what do you think, is this too harsh on Sam Altman or Carmen Ortiz ?
> Qwen barely moved toward Opus 4.8 in the earlier experiment, but moved by +20.58 points toward GPT-5.5 Pro here, including a large effect on the private synthetic puzzles. The data suggest that Qwen may have learned from GPT-5.5 Pro, or from a closely related GPT model, rather than from Opus.
News flash: people who scraped the Internet without permission to build their product complain when something vaguely similar is done to them. Water still wet, sky still blue. Film at 11.
(slibhb: Don't get me wrong, I agree with you 99%. But the frontier labs have zero moral authority here.)
I understand people just get off posting stuff like this. But creating LLMs from the entire corpus of human text was a huge achievement. Distilling those models is much less of an achievement. It means China is further behind than we thought.
First movers rarely take the prize, though, do they? Les Paul and Mary Ford pioneered overdubbing voices back in the 1940s and 50s. The Beatles stole it from Buddy Holly. And Elton John from the Beatles.
Distilling is a massive achievement. I can run Qwen. I can't run GPT (TM). It's not a matter of X is better than Y. It's a matter of Y exists, X does not.
I'd already stopped using Amazon for geopolitical reasons but I needed to get something in an emergency the last week (family member in the hospital, so I bent the rule) first time I'd seen Rufus, even if I wasn't boycotting Amazon for other reasons that monstrosity would have made me consider it.
I would not be surprised in the slightest if we later find out they are running those same open models to find useful traces or bits to incorporate into their own training. Lots of rules for thee but not for me from Big Ai
I look forward to a day when open models are so dominant that we stop considering traces to be some form of intellectual property that must be hidden from / manipulated for paying users.
It's that manipulation of inputs and outputs that really rubs me the wrong way
not just the internet, but every commercially published written work in existence, and I doubt their highly publicized destructive scanning thing had managed to legitimize even a fraction of a percent.
this what is permissible for Jupiter is not permissible for a cow bullshit alone should tell people all they need to know about what kind of greasy sociopaths run "open"ai and (mis)anthropic, and how seriously you should take their purported stances on "safety" and other self-serving shit.
That writing style might be a tad too tense
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)
they should call themselves real-time archaelogists: They dig up the past cause it's interest, but mostly meaningless and done by people with way too much funding for what they provide the rest of us with understanding.
I wasn't aware that we have access to raw reasoning tokens? I thought what you get is a kind of summary. Does the author have some kind of privileged access or was my assumption wrong?
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.
They reference this paper which describes a method to decrypt reasoning traces (by sending the encrypted trace back to the model and asking it to transcribe it):
https://stolen-thoughts.com/paper.pdf
Interesting, but I suppose that's a hole that can be easily patched.
patched with gpt 6
Reasoning works as long as there is a consistent latent space representation. Any kind of poison will just become part of the representation. There's evidence that even directly training on encrypted reasoning traces works, because the length is already a strong signal.
The problem with this is obviously that the only GPT 5.5 thoughts that we have access to are from stolen thought.
Qwen 3.8 0902 was trained after the release of the paper on August 10, so it should have seen those specific thoughts.
seems like only the companies in question could run this sort analysis long-term; since they have full access to their CoTs not in public datasets.
and we have to "trust them bro" to be fair and accurate, something I am very unlikely to do given their other false / misleading statements to date
And the only end result would be that the Chinese trained on their data just like OAI and Anthropic trained on our data so who cares
capitalism ensures I get high marx on my Ai bill
The thoughts trick was known before their paper / August.
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.
Sure, but TFA is trying to use Qwen's reaction to the thoughts as proof that they did indeed extract thoughts to train on.
My point is that any model trained after August 10 will know of those specific thoughts.
I'm sorry, it's going over my head still - my reading is "all models with any training after August 10 know how GPT 5.5 Pro thinks", but I'm not sure why - my initial guess was that's when GPT 5.5 was released, but that doesn't seem to be the case (it was released April 23rd).
They would know the specific thoughts released by the "stolen thought" paper, which became part of the public internet on August 10.
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.
Has the method for extracting the COT been blocked, now? Otherwise why could we not generate some fresh samples?
I'm not sure how viable it still is. Perhaps it's still possible, and perhas that's exacly what they did in wtich case my objection falls, but I don't know.
As a user of local models, does this mean that there are 'magic incantations' that can increase the performance of some local models?
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.
I don't think that follows from the published results. Would have been an interesting hypothesis to add though, and quite easy. Just throw the same setup at some benchmarks.
It's interesting that someone else noticed this. A week or two ago, GPT-5.6 Sol starting leaking reasoning into a tool call in Pi. I don't really know what happened, but it was ... interesting:
It eventually triggered some error state and stopped.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:
I passed the same prompt to Qwen, which opened with: This proves nothing, but it does seem an awful lot like they did use GTP-5.5/6 reasoning traces...I've read an embarrassing amount of Qwen 3.8 27b cot and it's nothing like this. I'm not refuting the OP, though, which is about continuation.
This is Unsloth's UD-Q4_K_S quantization (edit -- on llama.cpp, via the Vulkan backend, on an RX 7900 XT, with Unsloth's recommended sampler config), for "as replicable as LLMs can be" disclosure, done through llama.cpp's web UI. In another thread, via Open WebUI (both with no custom prompt, so whatever the "default" system prompt is), a reasoning block opens as such:
Now, I will add that when I use Qwen3.8 in a coding harness, the reasoning tends to NOT look like this, and instead looks more Qwen3.6-ish.But when I give short prompts to Qwen3.8 with no system prompt or previous instructions, it seems to strongly gravitate towards grug-talk.
I just tried to reproduce in Llama-cpp's webui: with the following (reverse-engineered) prompt:
“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.
Yeah, I dunno. For me it does "talk normally" for the most part when used in an actual coding harness.
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:
The person evaluating and noticing similar reasoning traces to gpt is because they are using a coding harness which probably has a different system prompt to llama webui which primarly serves as a chat interface
Same! and very surprised by it from day one of release.
Looks like we're a bunch of weirdos reading Qwen's CoT in here.
> Qwen3.8 27B's CoT seems ... very similar.
What? I've never seen garbled CoT like the one you posted when using Qwen3.8-27B.
I have seen plenty of Qwen 3.8 27B's caveman-like "Need doing this & that" thoughts. And on cerebras now I've seen them come real fast!
I wonder if that’s why 3.8 got so much better? Mixing the reasoning traces from both sides seems to be effective.
This makes me very sad
If Qwen and other Chinese labs are just copying reasoning traces, then those labs are more than a year behind the frontier.
It's been pretty obvious to me that the Chinese labs are operating mostly on a fast-follow strategy. The distillation attacks are well-documented, and there is good reason to believe they are able to copy architectural innovations as well. If US labs stagnate I would expect Chinese labs to stagnate as well. Their engineering is great, but in terms of frontier innovation (which requires heavy compute to search for new strategies that work at frontier scale) they are very far behind.
Japanese electronics started off the same way post-war.
The way I see it the Chinese labs are optimizing for other things, including effective compact models that don't need to run on top-of-the-line nVidia hardware.
It makes me happy because it means that these misanthropic technofascists have no moat. They can spend trillions of dollars only for it to be largely copied in short order.
Even if they weren't political adversaries of freedom, I would still feel 0% bad given all their training is already on data they got for free.
Information continues to want to be free. To the benefit of us all.
Preach brother, they stole everything on the internet, and beyond, to train their models. They thought all that information was free, and everyone a few months beyond them is just following their example.
They didn't actually steal in the sense that the information is still there on the internet.... About these shredded rare books, now we're talking.
If I may propose instead of "steal" I think we could agree to write they "Aaron-Swartz'ed" the information from the internet, what do you think, is this too harsh on Sam Altman or Carmen Ortiz ?
Is anything too harsh for these new robber barons?
Oh, we got a real rebel among us.
Well, yeah? I didn't think anybody seriously thought otherwise?
> Qwen barely moved toward Opus 4.8 in the earlier experiment, but moved by +20.58 points toward GPT-5.5 Pro here, including a large effect on the private synthetic puzzles. The data suggest that Qwen may have learned from GPT-5.5 Pro, or from a closely related GPT model, rather than from Opus.
How does this suggest anyting of the sorts?
Appendix B of https://stolen-thoughts.com/paper.pdf discusses this
> moved by +20.58 points toward GPT-5.5
Score go up. Probability go up. Conclusion.
News flash: people who scraped the Internet without permission to build their product complain when something vaguely similar is done to them. Water still wet, sky still blue. Film at 11.
(slibhb: Don't get me wrong, I agree with you 99%. But the frontier labs have zero moral authority here.)
I understand people just get off posting stuff like this. But creating LLMs from the entire corpus of human text was a huge achievement. Distilling those models is much less of an achievement. It means China is further behind than we thought.
First movers rarely take the prize, though, do they? Les Paul and Mary Ford pioneered overdubbing voices back in the 1940s and 50s. The Beatles stole it from Buddy Holly. And Elton John from the Beatles.
They're using competitor output as additional training input. Shrug.. It's not like they have access to the weights.
Distilling is a massive achievement. I can run Qwen. I can't run GPT (TM). It's not a matter of X is better than Y. It's a matter of Y exists, X does not.
How is China further behind if distillation cannot stop? I think it's a reasonable strategy to follow, even if they could train from scratch.
distillation results in a worse product than the actual teacher model iirc
I'd send them the worlds smallest violin but Rufus is getting in the way of me finding it.
I had to install an add-on in Waterfox to stop Rufus from following me around and interjecting every 2 minutes.
I'd already stopped using Amazon for geopolitical reasons but I needed to get something in an emergency the last week (family member in the hospital, so I bent the rule) first time I'd seen Rufus, even if I wasn't boycotting Amazon for other reasons that monstrosity would have made me consider it.
I would not be surprised in the slightest if we later find out they are running those same open models to find useful traces or bits to incorporate into their own training. Lots of rules for thee but not for me from Big Ai
I look forward to a day when open models are so dominant that we stop considering traces to be some form of intellectual property that must be hidden from / manipulated for paying users.
It's that manipulation of inputs and outputs that really rubs me the wrong way
They are copying useful parts of open models 100% especially from deepseek.
not just the internet, but every commercially published written work in existence, and I doubt their highly publicized destructive scanning thing had managed to legitimize even a fraction of a percent.
this what is permissible for Jupiter is not permissible for a cow bullshit alone should tell people all they need to know about what kind of greasy sociopaths run "open"ai and (mis)anthropic, and how seriously you should take their purported stances on "safety" and other self-serving shit.
For some reason, what China is doing seems worse. Part of it is that I want the US to stay ahead of China.