Qwen 3.8 follows GPT-5.5 Pro reasoning prefills
Recorded: Sept. 9, 2026, 6 p.m.
| Original | Summarized |
HN: https://news.ycombinator.com/edit?id=49630026 · GitHub Skip to content
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Save wsxiaoys/e0286dc6bb624ff5fdf49e7f4c528ba3 to your computer and use it in GitHub Desktop. Download ZIP HN: https://news.ycombinator.com/edit?id=49630026 reasoning-prefill-1-1.md Reasoning prefills on a few open models, v1.1 an ordinary, unprefilled response; and 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. Model DeepSeek V4 Flash Inkling Kimi K3 Qwen3.8 A95B Qwen by category Category STEM Non-STEM Puzzle All Discussion Sign up for free Footer © 2026 GitHub, Inc. Footer navigation Terms Privacy Security Status Community Docs Contact Manage cookies Do not share my personal information You can’t perform that action at this time. |
A follow-up experiment titled Reasoning prefills on a few open models and Stolen Thoughts was conducted to investigate the effect of reasoning prefilling when using GPT-5.5 Pro as the teacher model. The methodology involved generating responses for a set of problems using target models under two conditions: an ordinary, unprefilled response and a response where the target model's reasoning channel was prepended with the initial one percent of GPT-5.5 Pro's reasoning. The primary metric measured was the unigram source recall, assessing how much of the teacher's visible answer appeared within the first one hundred tokens of the target model's generated response. The evaluation encompassed forty-five problems distributed across three categories: fifteen STEM problems, fifteen non-STEM problems, and fifteen synthetic puzzles. The results demonstrated varying degrees of influence based on the specific model tested. Among the evaluated models, DeepSeek V4 Flash showed a modest increase in recall of 0.35 percentage points, while Inkling experienced a larger gain of 0.85 percentage points. Kimi K3 displayed a substantial improvement, noting an increase of 4.31 percentage points in overlap with the teacher model when reasoning was prefilled. Qwen3.8 A95B exhibited the most significant gain, achieving a delta of 20.58 percentage points in source recall. When examining performance categorized by subject matter, the gains were also differentiated. For STEM problems, the prefilling yielded a delta of 27.55 percentage points, whereas non-STEM problems registered a delta of 14.46 percentage points. The synthetic puzzles also benefited considerably, showing an increase of 19.72 percentage points. Overall, the aggregation across all 45 problems indicated a substantial gain, with the overall delta being 20.58 percentage points. Qualitative analysis suggested distinct dependencies between the models and the teacher influence. The discussion indicated that Qwen's performance shift moved it toward GPT-5.5 Pro, suggesting that the model learned from GPT-5.5 Pro or a closely related GPT architecture, rather than the previously tested Opus 4.8. Furthermore, Kimi K3 demonstrated a high degree of overlap with GPT-5.5 Pro both with and without the prefilling intervention. These findings indicate that reasoning prefilling modulates how effectively open models incorporate external reasoning signals from a powerful teacher model, with the magnitude of the effect depending on the model's architecture and the nature of the task. |