English(EN) 7.9% to 48.1%: LLM rewrote Qwen weights, no training

AI 模型性能通过直接权重编辑提升 6 倍，无需训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 19:29

研究人员展示了一种名为“权重空间编程”（Weight-Space Programming）的技术，可以在无需传统训练的情况下显著提高大型语言模型（LLM）的性能。通过直接编辑 Qwen3.5-0.8B 模型一小部分权重，该模型在 MATH-500 基准测试上的准确率从 7.9% 提高到 48.1%。这种方法通过识别和纠正导致循环行为的特定权重方向来工作，同时还提高了在 GSM8K 和 IFEval 基准测试上的性能。实验表明，当模型的潜在能力被重复输出等问题掩盖时，该技术是有效的；但当模型从根本上缺乏所需能力时，效果则不那么明显，Gemma 3 270M 模型实验也印证了这一点。 AI

影响展示了模型优化的一条新途径，绕过了传统的训练计算和时间。

排序理由研究论文详细介绍了一种无需训练即可提高 LLM 性能的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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dev.to — LLM tag TIER_1 English(EN) · frank chu · 2026-11-02 19:29

7.9% to 48.1%: LLM rewrote Qwen weights, no training

<p>Astra lifted Qwen3.5-0.8B from 7.9% to 48.1% on MATH-500 by editing 0.016% of its weights — no gradients, no training. That 6.1x jump is not a smarter model; it is a model finally allowed to finish its answers.</p> <p>The result comes from a research post published today, Nove…

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7.9% to 48.1%: LLM rewrote Qwen weights, no training

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