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。

Hacker News 最近的一篇帖文讨论了一种检测开源模型是否由闭源高端模型蒸馏而成的新技术。 研究人员利用一种已知的漏洞来提取 OpenAI GPT 和 Anthropic Claude 等模型的“思维链”(CoT)推理过程。通过截取闭源模型思维链的前 1%,并将其作为开源模型提示词的“预填充”内容,他们观察开源模型的表现是否与闭源模型相符。 研究作者运用此方法推测,Qwen 3.8 很可能是使用 GPT-5.5 Pro 生成的数据进行后训练的。然而,批评者认为这一结论尚无定论,并指出分析中所使用的特定 GPT-5.5 推理数据在 Qwen 3.8 发布前就已公开。这意味着该模型可能只是在常规训练过程中“见过”这些数据,而非明确通过蒸馏获得。
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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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