HN: https://news.ycombinator.com/edit?id=49630026

Reasoning prefills on a few open models, v1.1 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: an ordinary, unprefilled response; and 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. All problems 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 Qwen by category 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 Discussion 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.