Qwen 3.8 follows GPT-5.5 Pro reasoning prefills

Join the discussion

• Hacker News
• 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.)

  by CamperBob2
• 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...
  by spijdar
• 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.

  by hermitShell
• 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?
  by nzeid
• 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.

  by 7734128
• 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.

  by c7b
• For a personal project a while ago I peeked at Gemini's reasoning tokens in their coding CLI. I was pretty shocked. It was a bit like discovering Marvin (from Hitchhiker's Guide) was hiding in there all along. There was a lot of concern about my needs, "The user want's us to respond in a simple way...", "The user wants a clean front end...". Under the hood the poor model seemed very anxious to please with a hint of depression. It was a bit sad to see!
  by FailMore
• 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)

  by wongarsu