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English(EN) The Surprising Effectiveness of Deleting Weights in LLM Post-Training

新方法可实现大型语言模型技能习得，权重改动极小

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种名为“删除调优”（Deletion Tuning）的新方法，通过修改不到0.1%的权重，可以显著改变大型语言模型的能力。该技术通过将特定权重归零，在各种训练后模式下均能达到与完全微调相当的性能，同时保留更多模型的原始知识。另一种方法使用决策模型将工具调用等新技能提炼到Qwen 3.5 0.8B等小型大型语言模型中，实现了与LoRA相当的性能，延迟极低，且没有灾难性遗忘。 AI

影响 这些方法可能导致更高效的大型语言模型适应和技能习得，降低计算成本和内存需求。

排序理由 该集群描述了修改大型语言模型权重而无需大量训练的新颖研究论文和方法。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新方法可实现大型语言模型技能习得，权重改动极小

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报道来源 [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    删除 LLM 训练后权重惊人的有效性

    How little of a pretrained large language model (LLM) has to change for it to acquire new capabilities? We find that zeroing fewer than 0.05% of its weights, with no other modification, matches full fine-tuning (FFT) at scale across three LLM post-training regimes: reinforcement …

  2. r/LocalLLaMA TIER_1 English(EN) · /u/Thrumpwart ·

    删除 LLM 训练后权重出奇的有效性

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xswmb7/the_surprising_effectiveness_of_deleting_weights/"> <img alt="The Surprising Effectiveness of Deleting Weights in LLM Post-Training" src="https://external-preview.redd.it/q3evP6JeDpAC2MdSQHWYxnCYTqbJkE…

  3. r/LocalLLaMA TIER_1 English(EN) · /u/Nandakishor_ml ·

    决策模型直接编辑LLM权重 - 无需训练

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xski9d/decision_model_to_edit_llm_weight_directly_no/"> <img alt="Decision model to edit LLM weight directly - No training" src="https://preview.redd.it/t1xa0p9ssr0i1.png?width=640&amp;crop=smart&amp;auto=web…

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