Qwen 3.8 follows GPT-5.5 Pro reasoning prefills

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)

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.

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.

I see comments that this overlap between Qwen and GPT is due to rogue training or post hoc training. Did it occur to anyone that maybe the two sets of models were trained directly on the same solutions to the researchers' benchmark?

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.

While this result does imply there was some training on the reasoning trace and output of GPT 5.5, it doesn't tell us how much of the source of its training it was (even a small amount of post training could bump up the correlations in this way). And it doesn't tell us how much it is more a stylistic influence rather than being a genuine lifting over of intelligence.

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.

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.

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.

Could anyone explain to me the difference between thinking traces ("intermediate tokens") and the final responses? Specifically, why is it that Claude Opus 5's reasoning in Code is very easy to follow and sounds quite natural, while its answers are full of these very annoying AI-isms and sentence fragments that are void of meaning?

Are thinking traces and final answers trained for different objectives?

I wonder if that’s why 3.8 got so much better? Mixing the reasoning traces from both sides seems to be effective.

> 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?

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.)

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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.