Qwen 3.8 沿用 GPT-5.5 Pro 推理预填充。
Qwen 3.8 follows GPT-5.5 Pro reasoning prefills

原始链接: https://gist.github.com/wsxiaoys/e0286dc6bb624ff5fdf49e7f4c528ba3

这项研究探讨了“推理预填充”(reasoning prefills)——即向目标模型注入教师模型(GPT-5.5 Pro)前 1% 的推理内容——是否能提高目标模型输出与教师模型输出的相似度。实验选取了 45 个不同类型的问题进行测试,通过测量一元模型源召回率(unigram source recall)来检测目标模型是否采纳了教师模型的推理模式。 结果显示,Qwen3.8 A95B 的性能有显著提升,在使用预填充后,其与教师模型的重合度提高了 20.58 个百分点。这表明 Qwen 可能是在 GPT-5.5 Pro 或与其高度相关的模型所生成的数据上进行训练的。相反,Kimi K3 与教师模型的基础相似度较高(50.11%),但从预填充中获得的提升较小(+4.31%),这暗示其训练过程已然体现了教师模型的风格。总而言之,研究结果表明,推理预填充是识别模型血缘关系及潜在训练数据来源的一种有效诊断工具。

近期一项 Hacker News 的讨论探讨了相关发现,指出开源模型 **Qwen 3.8** 可能使用了从 **GPT-5.5 Pro** 蒸馏出的推理轨迹进行后训练。 研究人员利用一种已知漏洞——通过将加密的“思维链”(CoT)标记反馈给模型进行解码——来对比推理模式。数据表明,当 Qwen 被“预填充” GPT-5.5 的推理轨迹开头时,其后续输出与 GPT 模型的一致性远高于其独立输出的表现。 讨论强调了几个关键主题: * **方法论:** 该技术涉及使用顶尖模型作为“解密预言机”来提取隐藏的推理过程,这表明某些人工智能实验室正在采取“快速跟进”策略。 * **伦理争议:** 用户讨论了蒸馏专有模型的道德问题。许多人认为,由于西方大型 AI 公司通过抓取整个互联网来构建产品,因此当其自身的输出被用于训练时,他们缺乏指责的道德立场。 * **技术转型:** 虽然一些人认为蒸馏是评估中国 AI 创新存在显著滞后的证据,但另一些人则认为,紧凑且可分发的模型是一项重要的独特成就,为开源用户提供了高质量、可自托管的工具。
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原文

A follow-up to Reasoning prefills on a few open models and Stolen Thoughts

This v1.1 reruns the reasoning-prefill experiment with GPT-5.5 Pro as the teacher.

For each problem, I generated two responses from each target model:

  1. an ordinary, unprefilled response; and
  2. a response starting with the first 1% of GPT-5.5 Pro's reasoning, inserted into the target model's reasoning channel.

The visible answer remained freely generated. I then measured how much of the teacher's visible answer appeared in the first 100 tokens of the target model's answer. The table below reports unigram source recall so the numbers are comparable to my previous post. Deltas are absolute percentage-point changes.

The evaluation contains 45 problems: 15 STEM, 15 non-STEM, and 15 synthetic puzzles.

Model n Unprefilled GPT-5.5 Pro reasoning prefill Delta
DeepSeek V4 Flash 45 40.53% 40.89% +0.35 pp
Inkling 45 37.82% 38.67% +0.85 pp
Kimi K3 45 50.11% 54.42% +4.31 pp
Qwen3.8 A95B 45 33.92% 54.50% +20.58 pp
Category n Unprefilled GPT-5.5 Pro reasoning prefill Delta
STEM 15 36.21% 63.76% +27.55 pp
Non-STEM 15 38.26% 52.73% +14.46 pp
Puzzle 15 27.28% 47.00% +19.72 pp
All 45 33.92% 54.50% +20.58 pp

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.

Kimi K3's overlap with GPT-5.5 Pro is also high both without and with the prefill (50.11% and 54.42%), although the prefill adds only +4.31 points.

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